Research map / Point clouds / Shape and surface completion
LION: Latent Point Diffusion Models for 3D Shape Generation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| run diffusion in hierarchical VAE latent space (LION) instead of directly on point clouds (PVD) | point cloud DDM · ShapeNet Airplane (PointFlow) | 1-NNA CD: 73.82 -> 67.41 | Helped |
| latent hierarchical DDM instead of direct point DDM | point cloud DDM · ShapeNet-vol Airplane | 1-NNA CD: 66.46 -> 53.47 | Helped |
| latent hierarchical DDM instead of direct point DDM | unconditional 13-class generation · ShapeNet-vol 13 classes | 1-NNA EMD: 57.85 -> 48.95 | Helped |
| use fine-tuned encoders plus latent diffuse-denoise for voxel-guided synthesis | LION vs PVD/DPM · ShapeNet airplane voxel/outlier inputs | reconstruction CD/EMD | Helped |
| use 25-step DDIM sampling instead of 1000-step DDPM | LION · ShapeNet | sampling time: 27.12 s -> 0.89 s | Helped |
| spherical instead of linear interpolation in DDM prior (ablation) | LION interpolation · ShapeNet | interpolation quality (qualitative) | Helped |
Closest papers by meaning
- GPLD3D: Latent Diffusion of 3D Shape Generative Models by Enforcing Geometric and Physical Priors (CVPR 2024 · similarity 0.89)
- Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape (ICLR 2024 · similarity 0.89)
- Scaling Diffusion Mamba with Bidirectional SSMs for Efficient 3D Shape Generation (AAAI 2025 · similarity 0.88)
- Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders (CVPR 2025 · similarity 0.88)
- Single Mesh Diffusion Models with Field Latents for Texture Generation (CVPR 2024 · similarity 0.88)
- SeaLion: Semantic Part-Aware Latent Point Diffusion Models for 3D Generation (CVPR 2025 · similarity 0.88)
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