photometric_confidence & IndexError: min(): Expected reduction dim 0 to have non-zero size.

[iszhihao]

Hello, I use other pre-trained MVSNet models to generate depth maps and confidence values. However, the confidence values generated on the NerfSynth and TankandTemple datasets are all the same, as shown below.
photometric_confidence
tensor([[[0.1250, 0.1250, 0.1250, ..., 0.1250, 0.1250, 0.1250],
[0.1250, 0.1250, 0.1250, ..., 0.1250, 0.1250, 0.1250],
[0.1250, 0.1250, 0.1250, ..., 0.1250, 0.1250, 0.1250],
...,
Do you know how to resolve this issue? Here is the error message I received:
xyz_world_all torch.Size([0, 3]) torch.Size([0, 1]) torch.Size([0])
%%%%%%%%%%%%% getattr(dataset, spacemin, None) None
vishull_mask torch.Size([0])
alpha masking xyz_world_all torch.Size([0, 3]) torch.Size([0, 1])
Traceback (most recent call last):
File "/cluster/hebut/PointNeRF-v5/run/train_ft_nonstop.py", line 1109, in
main()
File "/cluster/hebut/PointNeRF-v5/run/train_ft_nonstop.py", line 653, in main
points_xyz_all, points_embedding_all, points_color_all, points_dir_all, points_conf_all, img_lst, c2ws_lst, w2cs_lst, intrinsics_all, HDWD_lst = gen_points_filter_embeddings(train_dataset, visualizer, opt)
File "/cluster/hebut/PointNeRF-v5/run/train_ft_nonstop.py", line 147, in gen_points_filter_embeddings
xyz_world_all, sparse_grid_idx, sampled_pnt_idx = mvs_utils.construct_vox_points_closest(xyz_world_all.cuda() if len(xyz_world_all) < 99999999 else xyz_world_all[::(len(xyz_world_all)//99999999+1),...].cuda(), opt.vox_res)
File "/cluster/hebut/PointNeRF-v5/run/../models/mvs/mvs_utils.py", line 541, in construct_vox_points_closest
xyz_min, xyz_max = torch.min(xyz, dim=-2)[0], torch.max(xyz, dim=-2)[0]
IndexError: min(): Expected reduction dim 0 to have non-zero size.
end loading