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About this paper
Dmitry Tochilkin, David Pankratz, Zexiang Liu, Zixuan Huang, Adam Letts, Yangguang Li, Ding Liang, Christian Laforte and 2 more
TripoSR reconstructs a 3D mesh from one object image in a single forward pass, avoiding per-image model optimization. It maps image features to a triplane and uses a NeRF to produce the mesh. The authors report around 0.5 seconds on an NVIDIA A100 GPU. On selected, aligned objects, it leads feed-forward baselines in shape metrics, not establishing performance on all images.
Key insights
- A carefully curated subset of the Objaverse dataset, focusing on diverse and high-quality 3D models, improved model generalization.
- The inclusion of a mask loss function during training helped reduce 'floater' artifacts and enhanced reconstruction fidelity.
- Local rendering supervision on random patches, rather than full high-resolution images, ensured faithful reconstruction of object surface details while reducing computational requirements.
Summary by alphaXiv.
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