mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2025-06-26 18:18:11 +00:00
add xy_utils with crop ply points for filter far scene
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Xun2001
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vendored
@ -6,3 +6,4 @@ diff_rasterization/diff_rast.egg-info
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diff_rasterization/dist
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tensorboard_3d
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screenshots
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data
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108
xy_utils/crop_points.py
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108
xy_utils/crop_points.py
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import argparse
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import numpy as np
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import open3d as o3d
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from scipy.spatial.transform import Rotation as R
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def load_camera_centers(images_txt_path):
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"""
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解析 COLMAP images.txt，计算每张图的相机光心。
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C = -R^T @ T，其中 R 由四元数 (qx, qy, qz, qw) 转换而来。
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"""
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centers = []
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with open(images_txt_path, 'r') as f:
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for line in f:
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if line.startswith('#') or not line.strip():
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continue
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elems = line.strip().split()
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if len(elems) < 10:
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continue
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qw, qx, qy, qz = map(float, elems[1:5])
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tx, ty, tz = map(float, elems[5:8])
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rot = R.from_quat([qx, qy, qz, qw]).as_matrix()
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centers.append((-rot.T @ np.array([tx, ty, tz])))
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return np.array(centers)
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def main(args):
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# 1. 加载相机中心
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camera_centers = load_camera_centers(args.images_txt)
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# 2. 分割地面平面，获取法线
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pcd = o3d.io.read_point_cloud(args.pcd_path)
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plane_model, inliers = pcd.segment_plane(
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distance_threshold=0.02, # 根据点云尺度调整
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ransac_n=3,
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num_iterations=1000
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)
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normal = np.array(plane_model[:3])
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normal /= np.linalg.norm(normal)
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# 3. 构造旋转矩阵，将地面法线对齐到 [0,0,1]
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target = np.array([0.0, 0.0, 1.0])
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axis = np.cross(normal, target)
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axis /= np.linalg.norm(axis)
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angle = np.arccos(np.dot(normal, target))
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R_align = o3d.geometry.get_rotation_matrix_from_axis_angle(axis * angle)
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# 4. 旋转点云与相机中心到“水平”坐标系
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pcd_aligned = pcd.rotate(R_align, center=(0,0,0))
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camera_centers_aligned = (R_align @ camera_centers.T).T
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# 5. 基于所有相机中心的 mean + max_offset 构建 AABB，裁剪
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center_mean = camera_centers_aligned.mean(axis=0)
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offsets = np.abs(camera_centers_aligned - center_mean)
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max_offset = offsets.max(axis=0)
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margin = args.margin
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min_bound = center_mean - max_offset - margin
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max_bound = center_mean + max_offset + margin
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aabb = o3d.geometry.AxisAlignedBoundingBox(min_bound, max_bound)
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pcd_cropped_aligned = pcd_aligned.crop(aabb)
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# 6. 逆旋转回原坐标系
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R_inv = R_align.T
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pcd_cropped = pcd_cropped_aligned.rotate(R_inv, center=(0,0,0))
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# 7. 构造 kept vs removed 掩码
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all_pts = np.asarray(pcd.points)
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inside_idx = set(aabb.get_point_indices_within_bounding_box(
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o3d.utility.Vector3dVector(all_pts)))
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mask_kept = np.array([i in inside_idx for i in range(len(all_pts))])
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kept_pts = all_pts[mask_kept]
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removed_pts= all_pts[~mask_kept]
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# 8. 统计并打印
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total, kept, removed = len(all_pts), len(kept_pts), len(removed_pts)
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print(f"总点数：{total}，保留：{kept}，删除：{removed}")
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# 9. 保存 COLMAP 格式 points3D.txt
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all_colors = np.asarray(pcd.colors) if pcd.has_colors() else np.ones((total,3))
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kept_colors = all_colors[mask_kept]
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with open(args.output_points_txt, "w") as f:
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f.write("# COLMAP points3D.txt\n")
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for idx, (pt, col) in enumerate(zip(kept_pts, kept_colors), start=1):
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r, g, b = (col * 255).astype(int)
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f.write(f"{idx} {pt[0]:.6f} {pt[1]:.6f} {pt[2]:.6f} {r} {g} {b} 0\n")
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# 10. 生成带红色删除点标记的 PLY
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removed_colors = np.tile([1.0,0.0,0.0], (removed,1))
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all_kept = kept_pts
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all_removed= removed_pts
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all_colors_marked = np.vstack([kept_colors, removed_colors])
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pcd_marked = o3d.geometry.PointCloud()
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pcd_marked.points = o3d.utility.Vector3dVector(
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np.vstack([all_kept, all_removed]))
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pcd_marked.colors = o3d.utility.Vector3dVector(all_colors_marked)
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o3d.io.write_point_cloud(args.output_ply, pcd_marked)
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if __name__ == "__main__":
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# 解析命令行参数
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parser = argparse.ArgumentParser(description="裁剪点云并保存为 COLMAP 格式和 PLY 文件")
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parser.add_argument("--images_txt", type=str, required=True, help="COLMAP images.txt 文件路径")
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parser.add_argument("--pcd_path", type=str, required=True, help="点云文件路径 (.pcd)")
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parser.add_argument("--output_points_txt", type=str, required=True, help="输出 COLMAP points3D.txt 文件路径")
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parser.add_argument("--output_ply", type=str, required=True, help="输出带标记的 PLY 文件路径")
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parser.add_argument("--margin", type=float, default=15.0, help="裁剪边距，默认为 15.0")
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args = parser.parse_args()
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# 执行主函数
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main(args)
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541
xy_utils/learning.ipynb
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541
xy_utils/learning.ipynb
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@ -0,0 +1,541 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "b66179c8",
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"metadata": {},
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"source": [
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"# 1.预处理数据"
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]
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},
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{
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"cell_type": "markdown",
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"id": "533db061",
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"metadata": {},
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"source": [
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"## 1.1 对点云数据进行处理 - 滤除环视范围外过远的点"
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]
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},
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{
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"cell_type": "markdown",
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"id": "864843b2",
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"metadata": {},
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"source": [
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"1. 解析 images.txt，计算所有相机在世界坐标系下的光心（位姿）。 \n",
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"2. 使用 Open3D 加载 .pcd 点云，并基于与最近相机光心的距离进行滤除。 \n",
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"3. 输出相机光心的范围（坐标轴最小/最大值）以及被滤除的点云数量。 \n",
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"4. 将保留的点云按 COLMAP points3D.txt 格式保存，并额外生成一个 PLY 文件，其中红色标记被删除的点，原色保留未删除点。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5dc3ac56",
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"metadata": {},
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"source": [
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"#### ipynb内部函数 -- 未进行地面与boundingbox对齐"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cc2e8c18",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import open3d as o3d\n",
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"from scipy.spatial.transform import Rotation as R\n",
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"\n",
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"def filter_point_cloud(images_txt, pcd_path, output_points_txt, output_points_ply, output_ply_path):\n",
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" \"\"\"\n",
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" 根据相机位姿过滤点云，并保存结果。\n",
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"\n",
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" Args:\n",
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" images_txt (str): 包含相机位姿的 images.txt 文件路径。\n",
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" pcd_path (str): 点云文件路径。\n",
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" output_points_txt (str): 保存过滤后点云的 points3D.txt 文件路径。\n",
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" output_ply_path (str): 保存带颜色标记的 PLY 文件路径。\n",
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" \"\"\"\n",
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" def load_camera_centers(images_txt):\n",
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" centers = []\n",
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" with open(images_txt, 'r') as f:\n",
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" for line in f:\n",
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" if line.startswith('#') or len(line.strip()) == 0:\n",
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" continue\n",
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" elems = line.strip().split()\n",
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" if len(elems) < 10:\n",
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" continue\n",
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" qw, qx, qy, qz = map(float, elems[1:5])\n",
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" tx, ty, tz = map(float, elems[5:8])\n",
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" rot = R.from_quat([qx, qy, qz, qw]).as_matrix()\n",
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" center = -rot.T @ np.array([tx, ty, tz])\n",
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" centers.append(center)\n",
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" return np.array(centers)\n",
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"\n",
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" # 1. 加载相机中心\n",
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" camera_centers = load_camera_centers(images_txt)\n",
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"\n",
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" # 2. 计算平均中心和最大偏移\n",
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" center_mean = camera_centers.mean(axis=0)\n",
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" offsets = np.abs(camera_centers - center_mean)\n",
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" max_offset = offsets.max(axis=0)\n",
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"\n",
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" # 3. 加载点云\n",
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" pcd = o3d.io.read_point_cloud(pcd_path)\n",
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" points = np.asarray(pcd.points)\n",
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" colors = np.asarray(pcd.colors) if pcd.has_colors() else np.ones_like(points)\n",
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"\n",
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" # 4. 构建包围盒并裁剪点云\n",
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" min_bound = center_mean - max_offset - 15\n",
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" max_bound = center_mean + max_offset + 15\n",
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" aabb = o3d.geometry.AxisAlignedBoundingBox(min_bound, max_bound)\n",
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" pcd_cropped = pcd.crop(aabb)\n",
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"\n",
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" # 5. 统计信息\n",
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" total_points = len(points)\n",
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" kept_points = np.asarray(pcd_cropped.points)\n",
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" kept_colors = np.asarray(pcd_cropped.colors) if pcd_cropped.has_colors() else np.ones_like(kept_points)\n",
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" removed_mask = np.ones(total_points, dtype=bool)\n",
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" kept_indices = aabb.get_point_indices_within_bounding_box(pcd.points)\n",
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" removed_mask[kept_indices] = False\n",
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" removed_points = points[removed_mask]\n",
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" removed_colors = colors[removed_mask]\n",
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"\n",
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" print(\"相机中心范围：\")\n",
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" print(f\" X: [{min_bound[0]:.3f}, {max_bound[0]:.3f}]\")\n",
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" print(f\" Y: [{min_bound[1]:.3f}, {max_bound[1]:.3f}]\")\n",
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" print(f\" Z: [{min_bound[2]:.3f}, {max_bound[2]:.3f}]\")\n",
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" print(f\"总点数：{total_points}\")\n",
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" print(f\"保留点数：{len(kept_points)}\")\n",
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" print(f\"删除点数：{len(removed_points)}\")\n",
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"\n",
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" # 6. 保存 points3D.txt (速度太慢，直接使用 ply )\n",
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" with open(output_points_txt, \"w\") as f:\n",
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" f.write(\"# 3D point list with one line of data per point:\\n\")\n",
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" f.write(\"# POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[]\\n\")\n",
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" for idx, (pt, col) in enumerate(zip(kept_points, kept_colors), start=1):\n",
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" r, g, b = (col * 255).astype(int)\n",
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" f.write(f\"{idx} {pt[0]:.6f} {pt[1]:.6f} {pt[2]:.6f} {r} {g} {b} 0\\n\")\n",
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" # 保存为 ply 文件\n",
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" pcd_kept = o3d.geometry.PointCloud()\n",
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" pcd_kept.points = o3d.utility.Vector3dVector(kept_points)\n",
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" pcd_kept.colors = o3d.utility.Vector3dVector(kept_colors)\n",
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" o3d.io.write_point_cloud(output_points_ply, pcd_kept)\n",
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" print(\"保留的点云已保存为 {output_points_ply}\")\n",
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" \n",
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" # 7. 保存带颜色的 PLY 文件\n",
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" all_points = np.vstack((kept_points, removed_points))\n",
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" removed_colors_red = np.tile([1.0, 0.0, 0.0], (len(removed_points), 1))\n",
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" all_colors = np.vstack((kept_colors, removed_colors_red))\n",
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" pcd_all = o3d.geometry.PointCloud()\n",
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" pcd_all.points = o3d.utility.Vector3dVector(all_points)\n",
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" pcd_all.colors = o3d.utility.Vector3dVector(all_colors)\n",
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" o3d.io.write_point_cloud(output_ply_path, pcd_all)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "80b924be",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"相机中心范围：\n",
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" X: [-0.200, 28.270]\n",
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" Y: [-16.082, 1.699]\n",
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" Z: [-9.392, 0.714]\n",
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"总点数：16404321\n",
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"保留点数：10684495\n",
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"删除点数：5719826\n",
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"保留的点云已保存为 {output_points_ply}\n"
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]
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}
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],
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"source": [
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"import os\n",
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"folder_path = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results'\n",
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"\n",
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"images_txt = os.path.join(folder_path, 'sparse/0/images.txt')\n",
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"pcd_path = os.path.join(folder_path, 'pcd/all_raw_points.pcd')\n",
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"output_points_ply = os.path.join(folder_path, 'pcd/points3D.ply')\n",
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"vis_ply_path = os.path.join(folder_path, 'pcd/vis_filter.ply')\n",
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"output_points_txt = os.path.join(folder_path, 'pcd/points3D.txt')\n",
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"filter_point_cloud(images_txt,pcd_path,output_points_txt, output_points_ply,vis_ply_path)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1a4206e8",
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"metadata": {},
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"source": [
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"#### 调用 crop_points.py 文件"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8d8c55ed",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"总点数：16404321，保留：15254842，删除：1149479\n"
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]
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}
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],
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"source": [
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"!python crop_points.py \\\n",
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" --images_txt /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/sparse/0/images.txt \\\n",
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" --pcd_path /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/all_raw_points.pcd \\\n",
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" --output_points_txt /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/points3D.txt \\\n",
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" --output_ply /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/filtered_colored.ply \\\n",
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" --margin 10.0"
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]
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},
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{
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"cell_type": "markdown",
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"id": "651ed72f",
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"metadata": {},
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"source": [
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"## 1.2 利用体素化降采样"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "56f5cba7",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"raw points len : 15254842\n",
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"downsample points len : 201667\n",
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"降采样后的点云已保存到 /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/sparse/0/points3D_filter_0.2.ply，体素大小：0.2\n"
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]
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}
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],
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"source": [
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"import os\n",
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"from tools.points_utils import voxel_downsample_and_save\n",
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"\n",
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"voxel_size = 0.2\n",
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||||
"folder_path = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results'\n",
|
||||
"input_ply_path = os.path.join(folder_path,'pcd/points3D.ply')\n",
|
||||
"output_ply_path = os.path.join(folder_path,f'pcd/points3D_{voxel_size}.ply')\n",
|
||||
"input_ply_path = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/sparse/0/points3D_filter.ply'\n",
|
||||
"output_ply_path = f'/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/sparse/0/points3D_filter_{voxel_size}.ply'\n",
|
||||
"\n",
|
||||
"voxel_downsample_and_save(voxel_size, input_ply_path, output_ply_path) # ply downsample to ply\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8544bb3e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# pcd 2 ply\n",
|
||||
"# 将原pcd点云，转换为ply点云\n",
|
||||
"from tools.points_utils import pcd_2_ply\n",
|
||||
"pcd_file = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/all_raw_points.pcd'\n",
|
||||
"ply_file = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/all_raw_points.ply'\n",
|
||||
"\n",
|
||||
"pcd_2_ply(pcd_file,ply_file)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "2bf16bb1",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
|
||||
"[Open3D INFO] WebRTC GUI backend enabled.\n",
|
||||
"[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n",
|
||||
"正在写入COLMAP TXT文件: /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/points3D_filter_white.txt\n",
|
||||
"Converted /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/sparse/0/points3D_filter.ply to Colmap TXT in: /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/points3D_filter_white.txt\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# pcd 2 txt\n",
|
||||
"from tools.points_utils import pcd_2_colmap_txt\n",
|
||||
"pcd_file = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/sparse/0/points3D_filter.ply'\n",
|
||||
"txt_file = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/points3D_filter_white.txt'\n",
|
||||
"\n",
|
||||
"pcd_2_colmap_txt(pcd_file, txt_file, is_white=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "6fd0b306",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"选择一个合适的 points3D.ply 文件复制到 sparse/0 下"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "22e87bd3",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 1.2 Resize for more images"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "91ce4cb8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"import cv2\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"\n",
|
||||
"def resize_images(input_dir, output_dir, extensions):\n",
|
||||
" \"\"\"\n",
|
||||
" 读取输入文件夹中的所有图片，调整为1/2大小后保存到输出文件夹\n",
|
||||
" \n",
|
||||
" Args:\n",
|
||||
" input_dir: 输入图片文件夹路径\n",
|
||||
" output_dir: 输出图片文件夹路径\n",
|
||||
" extensions: 支持的图片扩展名列表\n",
|
||||
" \"\"\"\n",
|
||||
" # 确保输出目录存在\n",
|
||||
" os.makedirs(output_dir, exist_ok=True)\n",
|
||||
" \n",
|
||||
" # 获取所有图片文件\n",
|
||||
" image_files = []\n",
|
||||
" for file in os.listdir(input_dir):\n",
|
||||
" if any(file.lower().endswith(ext) for ext in extensions):\n",
|
||||
" image_files.append(file)\n",
|
||||
" \n",
|
||||
" if not image_files:\n",
|
||||
" print(f\"在 {input_dir} 中未找到支持的图片文件\")\n",
|
||||
" return\n",
|
||||
" \n",
|
||||
" print(f\"找到 {len(image_files)} 张图片\")\n",
|
||||
" \n",
|
||||
" # 处理每张图片\n",
|
||||
" count = 0\n",
|
||||
" for file in tqdm(image_files, desc=\"处理中\"):\n",
|
||||
" input_path = os.path.join(input_dir, file)\n",
|
||||
" output_path = os.path.join(output_dir, file)\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" # 读取图片\n",
|
||||
" img = cv2.imread(input_path)\n",
|
||||
" if img is None:\n",
|
||||
" print(f\"警告: 无法读取图片 {input_path}，跳过\")\n",
|
||||
" continue\n",
|
||||
" \n",
|
||||
" # 获取原始尺寸\n",
|
||||
" height, width = img.shape[:2]\n",
|
||||
" \n",
|
||||
" # 计算新尺寸\n",
|
||||
" new_width = width // 2\n",
|
||||
" new_height = height // 2\n",
|
||||
" \n",
|
||||
" # 调整尺寸\n",
|
||||
" resized_img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_AREA)\n",
|
||||
" \n",
|
||||
" # 保存图片\n",
|
||||
" cv2.imwrite(output_path, resized_img)\n",
|
||||
" \n",
|
||||
" # 输出尺寸信息\n",
|
||||
" if count == 0:\n",
|
||||
" print(f\"{file}: {width}x{height} -> {new_width}x{new_height}\")\n",
|
||||
" count += 1\n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"错误: 处理图片 {input_path} 时出错: {str(e)}\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "e9b4da36",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"input_path = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01/depth_maps'\n",
|
||||
"output_path = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01/depth_maps_2'\n",
|
||||
"exts = ['jpg','jpeg','png']\n",
|
||||
"\n",
|
||||
"resize_images(input_path, output_path, exts)\n",
|
||||
"print(\"所有图片处理完成！\") "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3830cb91",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 2. LIVO2和Colmap的重建对比实验"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "7dd6ff70",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"colmap无法恢复相机位姿；所以这里我们使用livo2恢复位姿后，用colmap 进行三角测量获取关键点\n",
|
||||
"https://www.cnblogs.com/Todd-Qi/p/15080968.html"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "4ad9f127",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 2.1 基于Livo2位姿进行稀疏重建"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "40e8c7f1",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"colmap无法恢复相机位姿；所以这里我们使用livo2恢复位姿后，用colmap 进行三角测量获取关键点\n",
|
||||
"https://www.cnblogs.com/Todd-Qi/p/15080968.html"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "497d715a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"1. 准备来自Livo2的位姿和相机数据 cameras.txt, images.txt\n",
|
||||
" 将内参(camera intrinsics) 放入cameras.txt， 外参(camera extrinsics)放入 images.txt , points3D.txt 为空 \n",
|
||||
" - images.txt 中全部 0.0 0.0 -1 删除; \n",
|
||||
" - points3D.txt 内容清空;\n",
|
||||
" - cameras.txt 中的内参进行修改 (对输入图像全部进行了 resize 操作，因此需要修改相机内参，将fx, fy, cx, cy 都除以2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "497596e0",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"2. 特征匹配与特征提取 \n",
|
||||
"``` bash\n",
|
||||
" colmap feature_extractor \\\n",
|
||||
" --database_path /path/to/project/database.db \\ \n",
|
||||
" --image_path /path/to/project/images\n",
|
||||
"```\n",
|
||||
"``` bash\n",
|
||||
" colmap exhaustive_matcher \\\n",
|
||||
" --database_path /path/to/project/database.db\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "590c5ba8",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"3. 三角化重建 (保存的点云和其他文件均为bin格式)\n",
|
||||
"``` bash\n",
|
||||
" colmap point_triangulator \\\n",
|
||||
" --database_path /path/to/project/database.db \\\n",
|
||||
" --image_path /path/to/project/images \\\n",
|
||||
" --input_path /path/to/sparse_model \\\n",
|
||||
" --output_path /path/to/triangulated_model\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"查看txt结果\n",
|
||||
"``` bash\n",
|
||||
" colmap model_converter \\\n",
|
||||
" --input_path 0 \\\n",
|
||||
" --output_path 0_txt_from_livo2 \\\n",
|
||||
" --output_type TXT\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "6c2b853f",
|
||||
"metadata": {},
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d359ac68",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"4. 稠密重建(optional)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "77250b4b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 3.训练"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d44f71f7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# baseline raw gs for training\n",
|
||||
"!CUDA_VISIBLE_DEVICES=1 python train.py \\\n",
|
||||
" -s data/tree_01_livo2 \\\n",
|
||||
" -m data/tree_01_livo2/outputs/3dgs_baseline\n",
|
||||
" \n",
|
||||
"# render\n",
|
||||
"!CUDA_VISIBLE_DEVICES=1 python render.py \\\n",
|
||||
" -s data/tree_01_colmap \\\n",
|
||||
" -m data/tree_01_colmap/outputs/3dgs_baseline "
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.18"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
58
xy_utils/tools/points_utils.py
Normal file
58
xy_utils/tools/points_utils.py
Normal file
@ -0,0 +1,58 @@
|
||||
import open3d as o3d
|
||||
import numpy as np
|
||||
import os
|
||||
|
||||
def pcd_2_ply(pcd_file_path,ply_file_path):
|
||||
point_cloud = o3d.io.read_point_cloud(pcd_file_path)
|
||||
o3d.io.write_point_cloud(ply_file_path, point_cloud)
|
||||
print(f"Converted {pcd_file_path} to {ply_file_path}")
|
||||
|
||||
|
||||
def pcd_2_colmap_txt(pcd_file, txt_file, is_white=False):
|
||||
'''
|
||||
is_white: 是否将点云颜色设置为白色
|
||||
'''
|
||||
point_cloud = o3d.io.read_point_cloud(pcd_file)
|
||||
points = np.asarray(point_cloud.points)
|
||||
colors = np.asarray(point_cloud.colors)
|
||||
|
||||
|
||||
print(f"正在写入COLMAP TXT文件: {txt_file}")
|
||||
with open(txt_file, 'w') as f:
|
||||
f.write('# 3D point list with one line of data per point\n')
|
||||
f.write('# POINT_ID, X, Y, Z, R, G, B, ERROR\n')
|
||||
f.write('# Number of points: {}\n'.format(len(points)))
|
||||
for i, point in enumerate(points):
|
||||
x, y, z = point
|
||||
if is_white:
|
||||
r, g, b = 255, 255, 255
|
||||
else:
|
||||
r, g, b = colors[i]
|
||||
r,g,b = int(r * 255), int(g * 255), int(b * 255)
|
||||
error = 0
|
||||
# 空的观察列表 (IMAGE_ID, POINT2D_IDX)
|
||||
# 写入一行
|
||||
f.write(f"{i} {x:.6f} {y:.6f} {z:.6f} {r} {g} {b} {error}\n")
|
||||
|
||||
print(f"Converted {pcd_file} to Colmap TXT in: {txt_file}")
|
||||
|
||||
def voxel_downsample_and_save(voxel_size, input_ply_path, output_ply_path):
|
||||
"""
|
||||
从PLY文件读取点云，进行体素化降采样，并保存结果。
|
||||
|
||||
Args:
|
||||
input_ply_path (str): 输入PLY文件路径。
|
||||
voxel_size (float): 体素大小。
|
||||
output_ply_path (str): 保存降采样后点云的PLY文件路径。
|
||||
"""
|
||||
# 读取输入点云
|
||||
pcd = o3d.io.read_point_cloud(input_ply_path)
|
||||
print(f"raw points len : {len(pcd.points)}")
|
||||
|
||||
# 体素化降采样
|
||||
pcd_downsampled = pcd.voxel_down_sample(voxel_size)
|
||||
print(f"downsample points len : {len(pcd_downsampled.points)}")
|
||||
|
||||
# 保存降采样后的点云
|
||||
o3d.io.write_point_cloud(output_ply_path, pcd_downsampled)
|
||||
print(f"降采样后的点云已保存到 {output_ply_path}，体素大小：{voxel_size}")
|
||||
Loading…
Reference in New Issue
Block a user