场景重建作为3D检测的建图先验
Scene Reconstruction as Mapping Priors for 3D Detection
利用自动构建的密集建图先验来增强3D检测，为处理远距离或恶劣天气下的传感器稀疏与噪声问题提供了可扩展的解决方案，其建图先验构建流程和MPA3D融合框架可被其他自动驾驶感知任务借鉴。
原文依据 we propose a scalable solution to systemically leverage mapping to improve 3D detection
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论在自动驾驶中，建图对于运动规划至关重要，但作为感知任务（如3D目标检测）的资源仍未被充分利用。地图能够提供静态环境的鲁棒结构先验，适用于解决传感器数据稀疏或噪声引起的歧义——这些问题在远距离物体或恶劣天气条件下尤为普遍。然而，传统的高精地图获取和维护成本高昂，这给实现高效、大规模部署带来了挑战。本文提出一种可扩展的解决方案，通过克服两个主要挑战来系统性地利用建图改进3D检测。首先，我们引入一个流程，从聚合的传感器数据中自动构建密集建图先验，无需人工标注。其次，我们设计了一种新颖的建图先验增强3D检测（MPA3D）框架，以有效地将建图先验与不同模态的传感器数据融合。在Waymo开放数据集上的大量实验表明，我们的方法取得了新的最优结果，并证明了使用可扩展的重建场景先验来增强3D检测的有效性。
英文原文摘要 1236 字符
In autonomous driving, mapping is critical for motion planning but remains an under-utilized resource for perception tasks like 3D object detection. Maps can provide robust structural priors of the static environment, suited to resolving ambiguities and correcting for sensor data sparsity or noise -- issues especially prevalent for distant objects or during adverse weather conditions. However, conventional High-Definition (HD) maps are resource-intensive to obtain and maintain, which presents a challenge for achieving efficient, large-scale deployment. In this paper, we propose a scalable solution to systemically 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 Prior Augmented 3D detection (MPA3D) framework to effectively integrate the mapping priors with the distinct modalities of sensor data. Our extensive experiments on the Waymo Open Dataset demonstrate that our approach achieves new state-of-the-art results, and proving the effectiveness of using scalable, reconstructed scene priors to enhance 3D detection.