Summary of Lora3d: Low-rank Self-calibration Of 3d Geometric Foundation Models, by Ziqi Lu et al.
LoRA3D: Low-Rank Self-Calibration of 3D Geometric Foundation Models
by Ziqi Lu, Heng Yang, Danfei Xu, Boyi Li, Boris Ivanovic, Marco Pavone, Yue Wang
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); 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 paper introduces LoRA3D, a self-calibration pipeline that refines pre-trained 3D geometric foundation models for in-the-wild 3D vision tasks. The approach leverages robust optimization techniques to refine multi-view predictions and align them into a global coordinate frame, incorporating prediction confidence to generate high-quality pseudo labels for calibrating views. LoRA3D is an efficient method that completes the self-calibration process on a single standard GPU within 5 minutes, with each low-rank adapter requiring only 18MB of storage. The paper evaluates LoRA3D on over 160 scenes from various datasets, achieving up to 88% performance improvement for 3D reconstruction, multi-view pose estimation, and novel-view rendering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem with using pre-trained 3D models for real-world situations. These models struggle to work well in certain conditions like limited lighting or different views. The authors propose a new way to make these models better suited for specific scenes by refining their predictions and aligning them correctly. This process, called LoRA3D, is fast and efficient, taking just 5 minutes on a regular computer. The results show that this method can significantly improve the accuracy of 3D reconstruction, pose estimation, and novel-view rendering. |
Keywords
» Artificial intelligence » Optimization » Pose estimation
