Better Monocular 3D Detectors with LiDAR from the Past
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911833896517632 |
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| author | You, Yurong Phoo, Cheng Perng Diaz-Ruiz, Carlos Andres Luo, Katie Z Chao, Wei-Lun Campbell, Mark Hariharan, Bharath Weinberger, Kilian Q |
| author_facet | You, Yurong Phoo, Cheng Perng Diaz-Ruiz, Carlos Andres Luo, Katie Z Chao, Wei-Lun Campbell, Mark Hariharan, Bharath Weinberger, Kilian Q |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05139 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Better Monocular 3D Detectors with LiDAR from the Past You, Yurong Phoo, Cheng Perng Diaz-Ruiz, Carlos Andres Luo, Katie Z Chao, Wei-Lun Campbell, Mark Hariharan, Bharath Weinberger, Kilian Q Computer Vision and Pattern Recognition Robotics 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. |
| title | Better Monocular 3D Detectors with LiDAR from the Past |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2404.05139 |