Self-driving cars must detect vehicles, pedestrians, and other traffic participants accurately to operate safely. Small, far-away, or highly occluded objects are particularly challenging because there is limited information in the LiDAR point clouds for detecting them. To address this challenge, we leverage valuable information from the past: in particular, data collected in past traversals of the same scene. We posit that these past data, which are typically discarded, provide rich contextual information for disambiguating the above-mentioned challenging cases. To this end, we propose a novel, end-to-end trainable Hindsight framework to extract this contextual information from past traversals and store it in an easy-to-query data structure, which can then be leveraged to aid future 3D object detection of the same scene. We show that this framework is compatible with most modern 3D detection architectures and can substantially improve their average precision on multiple autonomous driving datasets, most notably by more than 300% on the challenging cases.

我们提出了一种新颖的端到端可训练的远见框架，从过去的数据中提取上下文信息，存储在易于查询的数据结构中，用于帮助未来对相同场景的三维物体探测。该框架与大多数现代3D检测架构兼容，并可以显著提高其对多个自动驾驶数据集的平均精度，尤其是在挑战性案例上提高了300%以上。

回顾思考：利用过去的遍历帮助3D感知