Reproduce “OCCDiff: Occupancy Diffusion Model for High-Fidelity 3D Building Reconstruction from Noisy Point Clouds” on OpenResearch
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About this paper
Jialu Sui, Rui Liu, Hongsheng Zhang
OCCDiff, from The University of Hong Kong, presents an occupancy diffusion model that reconstructs high-fidelity 3D building models from noisy point clouds. This method achieves state-of-the-art performance by leveraging latent diffusion in a continuous occupancy function space, producing geometrically accurate shapes with sharper features and improved robustness.
Key insights
- Occupancy-based latent features are more stable and effective for training diffusion models than point-based features, leading to smoother and less noisy latent spaces.
- Multi-task training, specifically including Chamfer Distance loss for the point encoder, significantly improves the overall performance and robustness of the diffusion model.
- Providing conditional features from the point encoder to both the latent encoder and occupancy decoder is essential for stable convergence and enhanced learning of global 3D shape distributions.
Summary by alphaXiv.
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