Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception
Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception
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DOI：
10.48550/arxiv.2203.11405
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Yurong You;Katie Luo;Xiangyu Chen;Junan Chen;Wei-Lun Chao;Wen Sun;Bharath Hariharan;Mark E. Campbell;Kilian Q. Weinberger
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文献类型：
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作者：
Yurong You;Katie Luo;Xiangyu Chen;Junan Chen;Wei-Lun Chao;Wen Sun;Bharath Hariharan;Mark E. Campbell;Kilian Q. Weinberger
自动驾驶汽车必须准确检测车辆、行人和其他交通参与者，以确保安全运行。小的、遥远的或高度遮挡的物体尤其具有挑战性，因为LiDAR点云中用于检测它们的信息有限。为了应对这一挑战，我们利用了过去的宝贵信息：特别是在过去对同一场景的遍历中收集的数据。我们认为，这些过去的数据，这是典型的丢弃，提供了丰富的上下文信息，消除上述具有挑战性的情况下。为此，我们提出了一种新颖的，端到端的可训练的后见之明框架，从过去的遍历中提取这种上下文信息，并将其存储在一个易于查询的数据结构中，然后可以利用它来帮助将来对同一场景的3D对象检测。我们表明，该框架与大多数现代3D检测架构兼容，并且可以大幅提高其在多个自动驾驶数据集上的平均精度，最明显的是在具有挑战性的情况下超过300%。
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.