licensed branch

SIBR_viewers

@@ -1 +1 @@
-Subproject commit d8856f60c5384cc1975439193bb627d77d917d77
+Subproject commit 4c4a95365597a78b105792794db70f09a1ece938

environment.yml

@@ -5,7 +5,6 @@ channels:
   - defaults
 dependencies:
   - cudatoolkit=11.6
-  - plyfile
   - python=3.7.13
   - pip=22.3.1
   - pytorch=1.12.1
@@ -18,3 +17,4 @@ dependencies:
     - submodules/fused-ssim
     - opencv-python
     - joblib
+    - meshio

scene/dataset_readers.py

@@ -19,7 +19,7 @@ from utils.graphics_utils import getWorld2View2, focal2fov, fov2focal
 import numpy as np
 import json
 from pathlib import Path
-from plyfile import PlyData, PlyElement
+import meshio
 from utils.sh_utils import SH2RGB
 from scene.gaussian_model import BasicPointCloud
 
@@ -118,29 +118,31 @@ def readColmapCameras(cam_extrinsics, cam_intrinsics, depths_params, images_fold
     return cam_infos
 
 def fetchPly(path):
-    plydata = PlyData.read(path)
-    vertices = plydata['vertex']
-    positions = np.vstack([vertices['x'], vertices['y'], vertices['z']]).T
-    colors = np.vstack([vertices['red'], vertices['green'], vertices['blue']]).T / 255.0
-    normals = np.vstack([vertices['nx'], vertices['ny'], vertices['nz']]).T
+    vertices = meshio.read(path)
+    positions = vertices.points
+    colors = np.vstack(
+        [
+            vertices.point_data['red'].astype(np.uint8),
+            vertices.point_data['green'].astype(np.uint8),
+            vertices.point_data['blue'].astype(np.uint8)
+        ]).T / 255.0
+    
+    normals = np.vstack([vertices.point_data['nx'], vertices.point_data['ny'], vertices.point_data['nz']]).T
     return BasicPointCloud(points=positions, colors=colors, normals=normals)
 
 def storePly(path, xyz, rgb):
-    # Define the dtype for the structured array
-    dtype = [('x', 'f4'), ('y', 'f4'), ('z', 'f4'),
-            ('nx', 'f4'), ('ny', 'f4'), ('nz', 'f4'),
-            ('red', 'u1'), ('green', 'u1'), ('blue', 'u1')]
-    
     normals = np.zeros_like(xyz)
+    point_data = {
+        "red": rgb[..., 0].astype(np.uint8),  
+        "green": rgb[..., 1].astype(np.uint8),  
+        "blue": rgb[..., 2].astype(np.uint8),  
+        "nx": normals[..., 0].astype(np.float32),  
+        "ny": normals[..., 1].astype(np.float32),  
+        "nz": normals[..., 2].astype(np.float32),  
+    }
 
-    elements = np.empty(xyz.shape[0], dtype=dtype)
-    attributes = np.concatenate((xyz, normals, rgb), axis=1)
-    elements[:] = list(map(tuple, attributes))
-
-    # Create the PlyData object and write to file
-    vertex_element = PlyElement.describe(elements, 'vertex')
-    ply_data = PlyData([vertex_element])
-    ply_data.write(path)
+    mesh = meshio.Mesh(points=xyz.astype(np.float32), point_data=point_data, cells=[])
+    meshio.write(path, mesh)
 
 def readColmapSceneInfo(path, images, depths, eval, train_test_exp, llffhold=8):
     try:

scene/gaussian_model.py

@@ -16,7 +16,7 @@ from torch import nn
 import os
 import json
 from utils.system_utils import mkdir_p
-from plyfile import PlyData, PlyElement
+import meshio
 from utils.sh_utils import RGB2SH
 from simple_knn._C import distCUDA2
 from utils.graphics_utils import BasicPointCloud
@@ -247,13 +247,14 @@ class GaussianModel:
         scale = self._scaling.detach().cpu().numpy()
         rotation = self._rotation.detach().cpu().numpy()
         
-        dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes()]
+        point_data = {}
+        attribs_no_pos = [attribute for attribute in self.construct_list_of_attributes() if attribute not in ["x", "y", "z"]]
+        values = np.concatenate((normals, f_dc, f_rest, opacities, scale, rotation), axis=1)
+        for index, attribute in enumerate(attribs_no_pos):
+            point_data[attribute] = values[..., index]
         
-        elements = np.empty(xyz.shape[0], dtype=dtype_full)
-        attributes = np.concatenate((xyz, normals, f_dc, f_rest, opacities, scale, rotation), axis=1)
-        elements[:] = list(map(tuple, attributes))
-        el = PlyElement.describe(elements, 'vertex')
-        PlyData([el]).write(path)
+        mesh = meshio.Mesh(points=xyz.astype(np.float32), point_data=point_data, cells=[])
+        meshio.write(path, mesh)
 
     def reset_opacity(self):
         opacities_new = self.inverse_opacity_activation(torch.min(self.get_opacity, torch.ones_like(self.get_opacity)*0.01))
@@ -261,7 +262,6 @@ class GaussianModel:
         self._opacity = optimizable_tensors["opacity"]
 
     def load_ply(self, path, use_train_test_exp = False):
-        plydata = PlyData.read(path)
         if use_train_test_exp:
             exposure_file = os.path.join(os.path.dirname(path), os.pardir, os.pardir, "exposure.json")
             if os.path.exists(exposure_file):
@@ -273,36 +273,36 @@ class GaussianModel:
                 print(f"No exposure to be loaded at {exposure_file}")
                 self.pretrained_exposures = None
 
-        xyz = np.stack((np.asarray(plydata.elements[0]["x"]),
-                        np.asarray(plydata.elements[0]["y"]),
-                        np.asarray(plydata.elements[0]["z"])),  axis=1)
-        opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis]
+        vertices = meshio.read(path)
+        xyz = vertices.points
+        point_data = vertices.point_data
+        opacities = np.asarray(point_data["opacity"])[..., np.newaxis]
 
         features_dc = np.zeros((xyz.shape[0], 3, 1))
-        features_dc[:, 0, 0] = np.asarray(plydata.elements[0]["f_dc_0"])
-        features_dc[:, 1, 0] = np.asarray(plydata.elements[0]["f_dc_1"])
-        features_dc[:, 2, 0] = np.asarray(plydata.elements[0]["f_dc_2"])
+        features_dc[:, 0, 0] = np.asarray(point_data["f_dc_0"])
+        features_dc[:, 1, 0] = np.asarray(point_data["f_dc_1"])
+        features_dc[:, 2, 0] = np.asarray(point_data["f_dc_2"])
 
-        extra_f_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("f_rest_")]
+        extra_f_names = [p for p in point_data if p.startswith("f_rest_")]
         extra_f_names = sorted(extra_f_names, key = lambda x: int(x.split('_')[-1]))
-        assert len(extra_f_names)==3*(self.max_sh_degree + 1) ** 2 - 3
+
         features_extra = np.zeros((xyz.shape[0], len(extra_f_names)))
         for idx, attr_name in enumerate(extra_f_names):
-            features_extra[:, idx] = np.asarray(plydata.elements[0][attr_name])
-        # Reshape (P,F*SH_coeffs) to (P, F, SH_coeffs except DC)
+            features_extra[:, idx] = np.asarray(point_data[attr_name])
+
         features_extra = features_extra.reshape((features_extra.shape[0], 3, (self.max_sh_degree + 1) ** 2 - 1))
 
-        scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")]
+        scale_names = [p for p in point_data if p.startswith("scale_")]
         scale_names = sorted(scale_names, key = lambda x: int(x.split('_')[-1]))
         scales = np.zeros((xyz.shape[0], len(scale_names)))
         for idx, attr_name in enumerate(scale_names):
-            scales[:, idx] = np.asarray(plydata.elements[0][attr_name])
+            scales[:, idx] = np.asarray(point_data[attr_name])
 
-        rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot")]
+        rot_names = [p for p in point_data if p.startswith("rot")]
         rot_names = sorted(rot_names, key = lambda x: int(x.split('_')[-1]))
         rots = np.zeros((xyz.shape[0], len(rot_names)))
         for idx, attr_name in enumerate(rot_names):
-            rots[:, idx] = np.asarray(plydata.elements[0][attr_name])
+            rots[:, idx] = np.asarray(point_data[attr_name])
 
         self._xyz = nn.Parameter(torch.tensor(xyz, dtype=torch.float, device="cuda").requires_grad_(True))
         self._features_dc = nn.Parameter(torch.tensor(features_dc, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True))