Omni-Scene: Omni-Gaussian Representation for Ego-Centric Sparse-View Scene Reconstruction
This work addresses a practical problem for autonomous driving scenarios by enabling more effective reconstruction from ego-centric views, though it is incremental as it builds on prior Gaussian representation methods.
The paper tackled ego-centric sparse-view scene reconstruction, which is challenging due to minimal cross-view overlap and frequent occlusions, by introducing an Omni-Gaussian representation that significantly outperforms state-of-the-art methods like pixelSplat and MVSplat in this task.
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.