Hi @fbxiang , thanks for sharing the codes. I did some simple experiments, and it seems that removing the cycle loss leads to better novel view synthesis (but maybe more distorted uv map). In Fig. 3 of the paper, removing cycle loss will lead to very bad mapping results which didn't happen in my experiments. May I know how to get the results in the last column of Fig. 3? Thanks.

Hello, Could you give me more detailed guidelines for using custom datasets? I tried to follow your guidelines, but I was confused about how to set these fields:

I also wonder should I make all of these files in the 'dtu_dataset.py' following above fields? self.campos = np.load(self.data_dir + "/in_camOrgs.npy") self.camat = np.load(self.data_dir + "/in_camAts.npy") self.focal = np.load(self.data_dir + "/in_camFocal.npy") self.princpt = np.load(self.data_dir + "/in_camPrincpt.npy") self.extrinsics = np.load(self.data_dir + "/in_camExtrinsics.npy") self.point_cloud = trimesh.load(self.data_dir + "/pcd_down_unit.ply") I guessed "pcd_down_unit.ply" is a polygon file of point clouds, but I don't know how to make other files.

I'd really appreciate it if you reply to my question.

Hi, thanks for the amazing work! I train the network on scan 114 data and got accurate rendering results like this: However, when I run visualize_nerf_atlas_radiance.py to visualize the geometry and texture map, I found they are strange. point cloud: mesh: texture map: