PanoOcc：面向基于相机的三维全景分割的统一占用表示
PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation
AI总结：
针对自动驾驶现有三维感知任务分散、缺乏统一端到端方案的问题，提出PanoOcc方法，通过体素查询粗到精聚合多帧多视图时空信息构建统一占用表示，在nuScenes和Occ3D基准上取得优异性能。
摘要：
对周围三维世界的全面建模是自动驾驶取得成功的关键。然而，现有的感知任务（如目标检测、道路结构分割、深度与高程估计以及开集目标定位）都仅聚焦于整体三维场景理解任务的一个小方面。这种分而治之的策略简化了算法开发流程，但代价是失去了该问题的端到端统一解决方案。在本研究中，我们通过研究基于相机的三维全景分割来解决这一局限，旨在为纯相机三维场景理解实现统一的占用表示。为此，我们提出了一种名为PanoOcc的新方法，该方法利用体素查询以从粗到精的策略聚合多帧多视图图像的时空信息，将特征学习与场景表示整合为统一的占用表示。我们开展了大量消融实验以验证所提方法的有效性与效率。我们的方法在nuScenes数据集上的基于相机的语义分割和全景分割任务中取得了新的最优结果。此外，我们的方法可轻松扩展到稠密占用预测任务，并在Occ3D基准上展现出可观的性能。代码将发布于https://github.com/Robertwyq/PanoOcc。
英文摘要：
Comprehensive modeling of the surrounding 3D world is key to the success of autonomous driving. However, existing perception tasks like object detection, road structure segmentation, depth & elevation estimation, and open-set object localization each only focus on a small facet of the holistic 3D scene understanding task. This divide-and-conquer strategy simplifies the algorithm development procedure at the cost of losing an end-to-end unified solution to the problem. In this work, we address this limitation by studying camera-based 3D panoptic segmentation, aiming to achieve a unified occupancy representation for camera-only 3D scene understanding. To achieve this, we introduce a novel method called PanoOcc, which utilizes voxel queries to aggregate spatiotemporal information from multi-frame and multi-view images in a coarse-to-fine scheme, integrating feature learning and scene representation into a unified occupancy representation. We have conducted extensive ablation studies to verify the effectiveness and efficiency of the proposed method. Our approach achieves new state-of-the-art results for camera-based semantic segmentation and panoptic segmentation on the nuScenes dataset. Furthermore, our method can be easily extended to dense occupancy prediction and has shown promising performance on the Occ3D benchmark. The code will be released at https://github.com/Robertwyq/PanoOcc.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
