Reproduce “Obj-NeRF: Extract Object NeRFs from Multi-view Images” on OpenResearch
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About this paper
Zhiyi Li, Lihe Ding, Tianfan Xue
Obj-NeRF extracts high-quality Neural Radiance Field (NeRF) representations of individual objects directly from multi-view images, bypassing the need for a pre-trained full-scene NeRF. The framework leverages a multi-view segmentation algorithm and optimizes NeRF training with depth supervision and ray pruning, enabling efficient and precise 3D object manipulation.
Key insights
- Integrating 2D segmentation with 3D-2D correspondence allows for robust and consistent object mask generation across multiple views, even in challenging scenes.
- Directly training an object-specific NeRF significantly outperforms approaches that segment from a pre-trained full-scene NeRF, particularly in terms of reconstruction quality and computational cost.
- Optimizing NeRF training through bounding box confinement, ray pruning, and depth supervision boosts efficiency and fidelity, enabling higher-resolution input images and better detail.
Summary by alphaXiv.
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