Summary of Toward General Object-level Mapping From Sparse Views with 3d Diffusion Priors, by Ziwei Liao et al.
Toward General Object-level Mapping from Sparse Views with 3D Diffusion Priors
by Ziwei Liao, Binbin Xu, Steven L. Waslander
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed General Object-level Mapping (GOM) system leverages a 3D diffusion model as shape prior to build accurate 3D maps of objects in a scene from sparse multi-view sensor observations. The GOM system incorporates Neural Radiance Fields (NeRFs) for both texture and geometry, enabling the estimation of object poses and shapes. A probabilistic optimization formulation is developed to fuse multi-view sensor observations with diffusion priors, allowing for joint 3D object pose and shape estimation. Experimental results demonstrate superior performance on real-world benchmarks compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GOM is a new way to create detailed maps of objects in a scene from camera views. It’s like putting together a puzzle! Previous methods had trouble mapping all the objects because they relied on having too many camera views, which isn’t always possible. The GOM system uses a special kind of model called a diffusion model as a starting point for creating the map. This helps to fill in gaps and make the map more accurate. It also uses something called Neural Radiance Fields (NeRFs) to capture texture and geometry details. The result is a highly detailed 3D map that can be used in various applications like robotics or computer vision. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Optimization
