提出全景高斯表示，解决驾驶视角下稀疏视图重建难题
Omni-Scene: Omni-Gaussian Representation for Ego-Centric Sparse-View Scene Reconstruction
- 设计全景高斯表示，适配自车视角下的低重叠、遮挡场景
- 在自车重建任务上超越SOTA方法，精度显著提升
- 兼顾自车与全局重建，适用多种实际驾驶场景
以往基于像素的高斯表示在前馈式稀疏视图重建中表现良好，但需依赖跨视角重叠以实现精确深度估计，且受物体遮挡和视锥截断影响。因此，这些方法需采用以场景为中心的数据采集方式，以保证跨视角重叠和完整可见性，限制了其在自车视角重建中的应用。而在自动驾驶场景中，更实用的范式是自车视角重建，其特征为跨视角重叠极少，且频繁出现遮挡与截断。现有像素表示的局限性制约了此前方法在此任务上的有效性。针对此问题，本文深入分析不同表示方式，提出专为网络设计的全景高斯表示，融合各表示优势并缓解其缺陷。实验表明，该方法在自车视角重建任务上显著优于当前最优方法pixelSplat和MVSplat，同时在以场景为中心的重建任务上达到可比性能。
原文摘要 · Abstract (English)
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.
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