Summary of Splatt3r: Zero-shot Gaussian Splatting From Uncalibrated Image Pairs, by Brandon Smart et al.
Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairs
by Brandon Smart, Chuanxia Zheng, Iro Laina, Victor Adrian Prisacariu
First submitted to arxiv on: 25 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 presents Splatt3R, a novel pose-free method for 3D reconstruction and novel view synthesis from stereo pairs. Unlike previous methods, Splatt3R predicts 3D Gaussian Splats without requiring camera parameters or depth information. Building upon the foundation of MASt3R, a 3D geometry reconstruction method, Splatt3R extends it to incorporate both 3D structure and appearance. The model is trained by optimizing geometry loss and then novel view synthesis objective, avoiding local minima present in training 3D Gaussian Splats from stereo views. The proposed loss masking strategy is crucial for strong performance on extrapolated viewpoints. The authors train Splatt3R on the ScanNet++ dataset and demonstrate excellent generalization to uncalibrated, in-the-wild images. The method can reconstruct scenes at 4FPS at 512 x 512 resolution, with resultant splats that can be rendered in real-time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to create 3D models and generate new views of objects from just two 2D pictures taken from different angles. The method, called Splatt3R, doesn’t need any information about the camera or the distance between objects. It builds upon an existing method for creating 3D point clouds and adds more details to make it more accurate. The authors train their model on a large dataset and show that it can generate high-quality 3D models from real-world images. This could be useful for applications like virtual reality, video games, or even self-driving cars. |
Keywords
» Artificial intelligence » Generalization
