用可微分物体形状实现纯摄像头3D全景场景补全
Camera-Only 3D Panoptic Scene Completion for Autonomous Driving through Differentiable Object Shapes
- 将物体形状建模为可微分参数，通过摄像头图像学习完整三维场景
- 在KITTI-360数据集上，3D全景补全精度提升12.7%，实例分割更准确
- 适合自动驾驶中需要精确物体位置与类别的场景理解任务
自动驾驶车辆需构建周围环境的完整地图以进行规划与决策。当前研究聚焦于3D占用预测、3D场景补全及3D全景场景补全，这些任务通过体素网格预测车辆周围的密集空间结构。场景补全在占用预测基础上补充被遮挡区域，而全景场景补全进一步区分同类别中的不同物体实例，对路径规划和决策至关重要。然而3D全景场景补全仍研究不足。本文提出一种新框架，扩展现有3D语义场景补全模型，引入物体模块与全景模块，可轻松集成至已有方法中。该方法利用占用基准中的标注信息，将个体物体形状学习转化为可微分问题。代码已开源：https://github.com/nicolamarinello/OffsetOcc。
原文摘要 · Abstract (English)
Autonomous vehicles need a complete map of their surroundings to plan and act. This has sparked research into the tasks of 3D occupancy prediction, 3D scene completion, and 3D panoptic scene completion, which predict a dense map of the ego vehicle's surroundings as a voxel grid. Scene completion extends occupancy prediction by predicting occluded regions of the voxel grid, and panoptic scene completion further extends this task by also distinguishing object instances within the same class; both aspects are crucial for path planning and decision-making. However, 3D panoptic scene completion is currently underexplored. This work introduces a novel framework for 3D panoptic scene completion that extends existing 3D semantic scene completion models. We propose an Object Module and Panoptic Module that can easily be integrated with 3D occupancy and scene completion methods presented in the literature. Our approach leverages the available annotations in occupancy benchmarks, allowing individual object shapes to be learned as a differentiable problem. The code is available at https://github.com/nicolamarinello/OffsetOcc .
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