Writing
The presentation of the overall architecture can be improved. Possible suggestions are to give an overview of the method in the beginning of Section 3 that the reader can follow, or to include notation in the figures that corresponds with what’s presented in the text.
More detail about mesh recovery procedure would be a good addition to the supplement. Currently it’s explained at a high level in the main text.
The proposed technique requires known camera intrinsics and camera pose. This potentially limits its applicability to highly controlled settings where such information is known.
Transformer-based techniques are known to be computationally intensive to train, and the current approach requires 8 GPUs for training. It would be good if the authors can also comment on the total training time of the model.
Update After Discussion Period
Thank you to the authors for engaging in thoughtful discussions with the reviewers. The authors addressed my concerns and no issues came up in the overall discussion that would cause me to reduce my rating. I therefore keep my initial rating: 7 - good paper, accept.
The authors have adequately addressed the limitations and potential negative societal impact of their work
7 - good paper, accept. The authors have adequately addressed the limitations and potential negative societal impact of their work