TACO：面向激光雷达定位与3D目标检测联合的任务感知对比学习
TACO: Task-Aware Contrastive Learning for Joint LiDAR Localization and 3D Object Detection
用对比学习显式解耦静态地理特征与目标中心特征，让定位与3D检测共享一个骨干网络并互为监督；做激光雷达定位或多任务BEV感知时，可借鉴这种按任务属性拆分特征空间、过滤动态噪声的思路。
原文依据 explicitly decouple and align static geographic features for localization and object-centric features for 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自动驾驶车辆要实现可靠导航与决策，既需要精确定位，也需要目标检测。传统上这两项任务被分开处理，导致计算冗余且跨任务知识迁移受限。本文提出 TACO，这是首个任务感知对比学习框架，在单一统一网络内联合完成激光雷达定位与3D目标检测。TACO 利用对比学习显式解耦并对齐用于定位的静态地理特征与用于检测的目标中心特征。这种双向互监督不仅通过过滤动态噪声提升了动态环境下的定位鲁棒性，还借助有效的空间上下文提高了检测精度。此外，我们提出 OxfoLD，这是首个提供多趟次激光雷达定位真值并带有丰富3D目标标注的数据集，从而支持在不同时间和天气条件下进行任务验证。实验结果表明，TACO 在保持有竞争力的检测性能的同时取得了最优的定位精度。代码与数据集将公开。
英文原文摘要 1189 字符
Reliable navigation and decision-making of autonomous vehicles require both accurate localization and object detection. Traditionally, these two tasks are handled separately, leading to redundant computation and limited cross-task knowledge transfer. This paper proposes TACO, the first Task-Aware COntrastive learning framework, which performs joint LiDAR localization and 3D object detection within a single, unified network. TACO leverages contrastive learning to explicitly decouple and align static geographic features for localization and object-centric features for detection. This bidirectional mutual supervision not only enhances localization robustness in dynamic environments by filtering dynamic noise but also boosts detection accuracy via effective spatial context. Additionally, we propose OxfoLD, the first dataset that provides multi-traversal LiDAR localization ground truth with rich 3D object annotations, thereby supporting task validation across various times and weather conditions. Experimental results demonstrate that TACO achieves state-of-the-art localization accuracy while maintaining competitive detection performance. The code and dataset will be released.