Omni-Scene: Omni-Gaussian Representation for Ego-Centric Sparse-View Scene Reconstruction

• URL: http://arxiv.org/abs/2412.06273v1
• Date: Mon, 09 Dec 2024 07:48:15 GMT
• Title: Omni-Scene: Omni-Gaussian Representation for Ego-Centric Sparse-View Scene Reconstruction
• Authors: Dongxu Wei, Zhiqi Li, Peidong Liu,
• Abstract summary: In autonomous driving scenarios, a more practical paradigm is ego-centric reconstruction, which is characterized by minimal cross-view overlap. This paper conducts an in-depth analysis of different representations, and introduces Omni-Gaussian representation with tailored network design. Experiments show that our method significantly surpasses state-of-the-art methods, pixelSplat and MVSplat, in ego-centric reconstruction.
• Score: 9.116550622312362
• License:
• Abstract: Prior works employing pixel-based Gaussian representation have demonstrated efficacy in feed-forward sparse-view reconstruction. However, such representation necessitates cross-view overlap for accurate depth estimation, and is challenged by object occlusions and frustum truncations. As a result, these methods require scene-centric data acquisition to maintain cross-view overlap and complete scene visibility to circumvent occlusions and truncations, which limits their applicability to scene-centric reconstruction. In contrast, in autonomous driving scenarios, a more practical paradigm is ego-centric reconstruction, which is characterized by minimal cross-view overlap and frequent occlusions and truncations. The limitations of pixel-based representation thus hinder the utility of prior works in this task. In light of this, this paper conducts an in-depth analysis of different representations, and introduces Omni-Gaussian representation with tailored network design to complement their strengths and mitigate their drawbacks. Experiments show that our method significantly surpasses state-of-the-art methods, pixelSplat and MVSplat, in ego-centric reconstruction, and achieves comparable performance to prior works in scene-centric reconstruction. Furthermore, we extend our method with diffusion models, pioneering feed-forward multi-modal generation of 3D driving scenes.

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