arXiv cs.AI· Dung Do Tien, Kien Nguyen Do Trung, Kiet Dang Vu, Trung Thai Tran, Duc Dung Nguyen·· 3 小时前AI 评分35
OccSplat：遮挡感知的高斯稠密化驾驶场景重建
Quo Vadis, Driving Scene Reconstruction? Occlusion-Aware Gaussian Densification
AI 导读
OccSplat 是一个前馈式 3D Gaussian Splatting 框架，通过遮挡感知高斯稠密化直接补全驾驶场景中缺失的 3D 几何。该方法先用 Gaussian Occlusion Mapping 识别缺乏高斯支持的遮挡区域，再用 Gaussian Occlusion Completion 生成新图元并整合回原表示，保持前馈推理、无需逐场景优化。
正文 · 原文
Abstract:While recent feed-forward 3D Gaussian Splatting methods enable efficient driving-scene reconstruction from surround-view images, their reliance on a single observation fundamentally hinders novel-view synthesis. Regions occluded in the input can become visible from novel viewpoints, leaving the reconstructed Gaussians under-constrained and incomplete. Moving beyond image-space inpainting for rendered views, we introduce OccSplat, a feed-forward framework whose core contribution is Occlusion-Aware Gaussian Densification, explicitly targeting missing scene geometry rather than missing pixels. By predicting new Gaussian primitives for unsupported regions, OccSplat completes the scene directly in the 3D representation. Specifically, OccSplat first identifies occluded regions that lack Gaussian support through Gaussian Occlusion Mapping. Gaussian Occlusion Completion then generates new primitives for these regions. The predicted primitives are integrated into the original representation to improve novel-view synthesis while retaining feed-forward inference without per-scene optimization. Experiments on the nuScenes benchmark demonstrate that OccSplat improves on the compared methods and yields particularly large gains in occluded regions, leading to more accurate and visually coherent driving-scene reconstruction.
| Comments: | Accepted at the Workshop on Computer Vision for Developing Countries (CV4DC), ACCV 2026 (Proceedings Track) |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.06273 [cs.CV] |
| (or arXiv:2611.06273v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06273 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Kiet Dang Vu [view email]
[v1]
Wed, 4 Nov 2026 10:17:04 UTC (6,940 KB)
来源：arXiv cs.AI · arxiv.org