用自动构建的地图先验提升3D目标检测，尤其在远距离和恶劣天气下更准
Scene Reconstruction as Mapping Priors for 3D Detection
- 从传感器数据自动构建密集地图先验，无需人工标注
- 在Waymo数据集上达到新最好效果，远距离检测准确率显著提升
- 适合自动驾驶系统中需高效部署的3D感知场景
在自动驾驶中，地图对路径规划至关重要，但感知任务如3D目标检测却未充分使用地图资源。地图可提供静态环境的可靠结构先验，帮助解决歧义并校正传感器数据稀疏或噪声问题，尤其在远距离或恶劣天气条件下。然而，传统高精地图获取与维护成本高，难以大规模应用。本文提出一种可扩展方案，通过克服两大挑战来系统性利用地图增强3D检测：首先，设计管道自动从聚合传感器数据构建密集地图先验，避免人工标注；其次，提出新型映射先验增强3D检测（MPA3D）框架，有效融合不同传感器模态与地图先验。在Waymo Open Dataset上的大量实验表明，该方法达到新最优性能，验证了可扩展重建场景先验在提升3D检测中的有效性。
原文摘要 · Abstract (English)
In autonomous driving, mapping is critical for motion planning but remains an under-utilized resource for perception tasks such as 3D object detection. Maps can provide robust structural priors of the static environment, helping resolve ambiguities and correct for sensor data sparsity or noise, especially for distant objects or under adverse weather conditions. However, conventional High-Definition (HD) maps are resource-intensive to obtain and maintain, which presents a challenge for efficient, large-scale deployment. In this paper, we propose a scalable solution to systematically leverage mapping to improve 3D detection by overcoming two primary challenges. First, we introduce a pipeline to automatically build dense mapping priors from aggregated sensor data, eliminating the need for human labeling. Second, we design a novel Mapping Priors Augmented 3D Detection (MPA3D) framework to effectively integrate mapping priors with different sensor modalities. Extensive experiments on the Waymo Open Dataset demonstrate that our approach achieves new state-of-the-art results, proving the effectiveness of scalable reconstructed scene priors for enhancing 3D detection.
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