GUIDE：面向自动驾驶增强障碍物感知的高斯统一实例检测方法
GUIDE: Gaussian Unified Instance Detection for Enhanced Obstacle Perception in Autonomous Driving
AI总结：
针对自动驾驶中传统障碍物检测方法难以表征不规则物体、计算开销大的问题，提出GUIDE框架，结合3D高斯与稀疏表征实现实例检测、占据预测与跟踪，在nuScenes上mAP提升50%，兼顾精度与效率。
摘要：
在自动驾驶领域，精准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边界框表征障碍物，往往难以捕捉现实世界中不规则形状物体的复杂特性。为突破这些局限，我们提出GUIDE，这一全新框架利用3D高斯开展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。该框架采用稀疏表征策略，通过高斯-体素投影（Gaussian-to-Voxel Splatting）提供细粒度实例级占据数据，无需承担密集体素网格带来的高额计算开销。在nuScenes数据集上的实验验证了GUIDE的性能，其实例占据平均精度（mAP）达21.61，相比现有方法提升50%，同时具备极具竞争力的跟踪能力。GUIDE在自动驾驶感知系统中建立了新基准，有效兼顾精度与计算效率，可更好应对真实驾驶环境的复杂场景。
英文摘要：
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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