Summary of Sparp: Fast 3d Object Reconstruction and Pose Estimation From Sparse Views, by Chao Xu et al.
SpaRP: Fast 3D Object Reconstruction and Pose Estimation from Sparse Views
by Chao Xu, Ang Li, Linghao Chen, Yulin Liu, Ruoxi Shi, Hao Su, Minghua Liu
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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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 This paper proposes a novel method called SpaRP to reconstruct 3D textured meshes and estimate camera poses from sparse-view 2D images. The input consists of one or a few unposed images of a single object with little or no overlap. The method distills knowledge from 2D diffusion models, fine-tunes them to deduce 3D spatial relationships, and jointly predicts surrogate representations for camera poses and multi-view images. These predictions are used to accomplish 3D reconstruction and pose estimation, which can be refined further. The proposed method significantly outperforms baseline methods in terms of 3D reconstruction quality and pose prediction accuracy while being efficient, requiring around 20 seconds to produce a textured mesh and camera poses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to take a few 2D pictures of an object from different angles and create a 3D model of the object. The method uses special algorithms that help the computer understand the relationships between the 2D images and creates a 3D picture that is very detailed and accurate. This can be useful for applications like virtual reality, robotics, and video games. |
Keywords
» Artificial intelligence » Diffusion » Pose estimation
