Accurate 3D object detection is crucial to autonomous driving. Though
LiDAR-based detectors have achieved impressive performance, the high cost of
LiDAR sensors precludes their widespread adoption in affordable vehicles.
Camera-based detectors are cheaper alternatives but often suffer inferior
performance compared to their LiDAR-based counterparts due to inherent depth
ambiguities in images. In this work, we seek to improve monocular 3D detectors
by leveraging unlabeled historical LiDAR data. Specifically, at inference time,
we assume that the camera-based detectors have access to multiple unlabeled
LiDAR scans from past traversals at locations of interest (potentially from
other high-end vehicles equipped with LiDAR sensors). Under this setup, we
proposed a novel, simple, and end-to-end trainable framework, termed
AsyncDepth, to effectively extract relevant features from asynchronous LiDAR
traversals of the same location for monocular 3D detectors. We show consistent
and significant performance gain (up to 9 AP) across multiple state-of-the-art
models and datasets with a negligible additional latency of 9.66 ms and a small
storage cost.

通过利用无标签的历史雷达数据，我们提出了一种新颖、简洁且端到端可训练的框架 AsyncDepth，用于改进单目 3D 检测器的性能，并取得了一致且显著的性能提升。