用3D高斯表示障碍物，提升自动驾驶感知精度与效率
GUIDE: Gaussian Unified Instance Detection for Enhanced Obstacle Perception in Autonomous Driving
- 用3D高斯替代传统框，实现更精细的障碍物建模
- 在nuScenes上实例占据mAP达21.61，比之前方法提升50%
- 兼顾高精度与低计算开销，适合实时自动驾驶系统
在自动驾驶领域，准确检测周围障碍物对有效决策至关重要。传统方法主要依赖3D边界框表示障碍物，难以捕捉真实世界中不规则物体的复杂形状。为此，我们提出GUIDE框架，采用3D高斯进行实例检测与占据预测。与传统占据预测方法不同，GUIDE还具备鲁棒的跟踪能力。该框架采用稀疏表示策略，通过高斯到体素的投射（Gaussian-to-Voxel Splatting）提供细粒度、实例级占据数据，避免了密集体素网格带来的计算负担。在nuScenes数据集上的实验验证表明，GUIDE实现了21.61的实例占据mAP，相比现有方法提升50%，同时保持良好的跟踪性能。GUIDE为自动驾驶感知系统树立了新基准，有效结合了精度与计算效率，更好应对真实道路环境的复杂性。
原文摘要 · Abstract (English)
In the realm of autonomous driving, accurately detecting surrounding obstacles is crucial for effective decision-making. Traditional methods primarily rely on 3D bounding boxes to represent these obstacles, which often fail to capture the complexity of irregularly shaped, real-world objects. To overcome these limitations, we present GUIDE, a novel framework that utilizes 3D Gaussians for instance detection and occupancy prediction. Unlike conventional occupancy prediction methods, GUIDE also offers robust tracking capabilities. Our framework employs a sparse representation strategy, using Gaussian-to-Voxel Splatting to provide fine-grained, instance-level occupancy data without the computational demands associated with dense voxel grids. Experimental validation on the nuScenes dataset demonstrates GUIDE's performance, with an instance occupancy mAP of 21.61, marking a 50\% improvement over existing methods, alongside competitive tracking capabilities. GUIDE establishes a new benchmark in autonomous perception systems, effectively combining precision with computational efficiency to better address the complexities of real-world driving environments.
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