自制的数据集在运行python run.py时出错

[zouchen92]

您好，我在自制数据集运行时出现了一些问题，想跟您请教一下。

背景：
我是在实验室内拍摄了实验室一周（包括两排桌椅、电脑以及天花板、地板）照片，有水平移动拍摄的、有俯拍和仰拍的，也有环绕着拍摄的，一共411张图。
对于拍摄的图片，利用colmap保存了database.db，然后进行了特征提取、特征匹配以及稀疏重建，最后Export model将其保存在"sparse/0/"文件夹下。
利用Fyusion/LLFF将导出的位姿数据转换成llff数据格式，由于照片数（411）和实际在colmap注册的数量（401）不匹配，因此出现了ERROR: the correct camera poses for current points cannot be accessed 这个错误，利用Fyusion/LLFF#60 (comment) 解决了问题，得到了poses_bounds.npy，并删掉了colmap未注册的照片。

问题：
运行 python run.py --config configs/custom/room.py 时，报错如下：

Loading images from data/room/dense/images_2
Loaded image data (2880, 1620, 3, 401) [2880. 1620. 2119.14228993]
Loaded data/room/dense 8.725662744276601e-10 38.88514969557468
recentered (3, 5)
[[ 1.0000000e+00 7.9511068e-08 7.2999038e-09 -8.4269325e+01]
[-7.9511068e-08 1.0000000e+00 -1.7426238e-07 3.5367581e+02]
[-7.2999176e-09 1.7426238e-07 1.0000000e+00 -1.4172569e+02]]
/home/vcis6/Userlist/Zouchen/LargeScaleNeRFPytorch/lib/load_llff.py:409: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at /opt/conda/conda-bld/pytorch_1634272204863/work/torch/csrc/utils/tensor_new.cpp:201.)
render_poses = torch.Tensor(render_poses)
Data:
(401, 3, 5) (401, 2880, 1620, 3) (401, 2)
HOLDOUT view is 214
Loaded llff (401, 2880, 1620, 3) torch.Size([120, 3, 5]) [2880. 1620. 2119.1423] data/room/dense
DEFINING BOUNDS
NEAR FAR 0.0 1.0
train: start
compute_bbox_by_cam_frustrm: start
/home/vcis6/anaconda3/envs/mega-nerf/lib/python3.9/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1634272204863/work/aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
compute_bbox_by_cam_frustrm: xyz_min tensor([nan, nan, nan])
compute_bbox_by_cam_frustrm: xyz_max tensor([nan, nan, nan])
compute_bbox_by_cam_frustrm: finish
train: skip coarse geometry searching
scene_rep_reconstruction (fine): train from scratch
scene_rep_reconstruction (fine): use multiplane images
dmpigo: world_size tensor([-9223372036854775808, -9223372036854775808, 256])
dmpigo: voxel_size_ratio 1.0
Traceback (most recent call last):
File "/home/vcis6/Userlist/Zouchen/LargeScaleNeRFPytorch/run.py", line 630, in
train(args, cfg, data_dict)
File "/home/vcis6/Userlist/Zouchen/LargeScaleNeRFPytorch/run.py", line 562, in train
scene_rep_reconstruction(
File "/home/vcis6/Userlist/Zouchen/LargeScaleNeRFPytorch/run.py", line 328, in scene_rep_reconstruction
model, optimizer = create_new_model(cfg, cfg_model, cfg_train, xyz_min, xyz_max, stage, coarse_ckpt_path)
File "/home/vcis6/Userlist/Zouchen/LargeScaleNeRFPytorch/run.py", line 266, in create_new_model
model = dmpigo.DirectMPIGO(
File "/home/vcis6/Userlist/Zouchen/LargeScaleNeRFPytorch/lib/dmpigo.py", line 41, in init
self.density = grid.create_grid(
File "/home/vcis6/Userlist/Zouchen/LargeScaleNeRFPytorch/lib/grid.py", line 29, in create_grid
return DenseGrid(**kwargs)
File "/home/vcis6/Userlist/Zouchen/LargeScaleNeRFPytorch/lib/grid.py", line 45, in init
self.grid = nn.Parameter(torch.zeros([1, channels, *world_size]))
RuntimeError: Trying to create tensor with negative dimension -9223372036854775808: [1, 1, -9223372036854775808, -9223372036854775808, 256]

（在这之前，我在单个方向拍了38张照片，然后利用上面相同的步骤，可以顺利生成模型以及视频）

[sjtuytc]

我感觉可能是你的colmap有问题，照片是不能随便拍的，一开始一定要可控，LLFF的教程里面有最早的要求。然后可以可视化一下姿态和点云，来决定一下是不是pose差很多导致的。

[sjtuytc]

Closed because of no response. Please reopen if the above solutions do not address your problems.

[zouchen92]

感谢您的解答