Summary of Unigaussian: Driving Scene Reconstruction From Multiple Camera Models Via Unified Gaussian Representations, by Yuan Ren et al.
UniGaussian: Driving Scene Reconstruction from Multiple Camera Models via Unified Gaussian Representations
by Yuan Ren, Guile Wu, Runhao Li, Zheyuan Yang, Yibo Liu, Xingxin Chen, Tongtong Cao, Bingbing Liu
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach to urban scene reconstruction in autonomous driving is proposed, addressing the lack of consideration for fisheye cameras in existing methods. The UniGaussian framework learns a unified 3D Gaussian representation from multiple camera models, including pinhole and fisheye cameras, using differentiable rendering and affine transformations. This allows for real-time rendering while maintaining differentiability. The framework is designed to learn holistic driving scene understanding by applying affine transformations to adapt different camera models and regularizing shared Gaussians with supervision from various modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a way to reconstruct urban scenes in autonomous driving simulations using fisheye cameras, which are commonly used in real-world applications. The method, called UniGaussian, uses a new approach to learn a unified representation of 3D data from multiple camera types and modalities like depth, semantic, normal, and LiDAR point clouds. |
Keywords
» Artificial intelligence » Scene understanding
