run succeed

gaussian_renderer/__init__.py

@@ -45,7 +45,8 @@ def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor,
         sh_degree=pc.active_sh_degree,
         campos=viewpoint_camera.camera_center,
         prefiltered=False,
-        debug=pipe.debug
+        debug=pipe.debug,
+        clamp_color=True
     )
 
     rasterizer = GaussianRasterizer(raster_settings=raster_settings)

run_code.txt

@@ -1,7 +1,7 @@
 Running:
 # Train with train/test split
 python train.py --source_path <path to COLMAP or NeRF Synthetic dataset> --model_path <保存路径> --eval
-python train.py --source_path /media/liuzhi/b4608ade-d2e0-430d-a40b-f29a8b22cb8c/Dataset/3DGS_Dataset/湘家荡 --model_path output/xiangjiadang --eval --resolution 1
+python train.py --source_path ../../Dataset/3DGS_Dataset/科技馆 --model_path output/kejiguan --eval --resolution 1
 
 --source_path / -s：COLMAP 或合成 Synthetic NeRF data set的源目录的路径。COLMAP类型包含 images/, sparse/0
 --model_path / -m：训练模型的存储路径，默认为 output/<random>

run_train_ours.py

@@ -0,0 +1,12 @@
+# python train.py --source_path ../../Dataset/3DGS_Dataset/凌公塘 --model_path output/linggongtang --eval --resolution 1
+# scene: {'科技馆': 'kejiguan', '万佛塔': 'wanfota', '植物': 'zhiwu', '凌公塘': 'linggongtang', '湘家荡': 'xiangjiadang', '寺平古宅': 'sipingguzhai'}
+#      : {'科技馆': ['kejiguan', 'cuda'], '万佛塔': ['wanfota', 'cuda'], '植物': ['zhiwu', 'cuda'], '凌公塘': ['linggongtang', 'cpu'], '湘家荡': ['xiangjiadang', 'cpu'], '寺平古宅': ['sipingguzhai', 'cpu']}
+# device = cuda: 科技馆、万佛塔、植物
+#        = cpu:  凌公塘、湘家荡、寺平古宅
+
+import os
+
+for cuda, scene in enumerate({'科技馆': ['kejiguan', 'cuda'], '湘家荡': ['xiangjiadang', 'cpu'], '凌公塘': ['linggongtang', 'cpu'], '寺平古宅': ['sipingguzhai', 'cpu'],}.items()):
+    print('---------------------------------------------------------------------------------')
+    one_cmd = f'python train.py --source_path ../../Dataset/3DGS_Dataset/{scene[0]} --model_path output/{scene[1][0]} --data_device "{scene[1][1]}" --resolution 1 --eval'
+    os.system(one_cmd)

scene/dataset_readers.py

@@ -109,8 +109,11 @@ def readColmapCameras(cam_extrinsics, cam_intrinsics, images_folder):
             # 如果不是以上两种模型，抛出错误
             assert False, "Colmap camera model not handled: only undistorted datasets (PINHOLE or SIMPLE_PINHOLE cameras) supported!"
 
-        image_path = os.path.join(images_folder, os.path.basename(extr.name))
+        image_path = os.path.join(images_folder, extr.name)
         image_name = os.path.basename(image_path).split(".")[0]
+
+        if not os.path.exists(image_path):
+            continue
         image = Image.open(image_path)
 
         cam_info = CameraInfo(uid=uid, R=R, T=T, FovY=FovY, FovX=FovX, image=image,
@@ -118,6 +121,7 @@ def readColmapCameras(cam_extrinsics, cam_intrinsics, images_folder):
         cam_infos.append(cam_info)
     # 在读取完所有相机信息后换行
     sys.stdout.write('\n')
+    print("valid Colmap camera size: {}".format(len(cam_infos)))
 
     # 返回整理好的相机信息列表
     return cam_infos