Sparse Points to Dense Clouds: Enhancing 3D Detection with Limited LiDAR Data

• URL: http://arxiv.org/abs/2404.06715v1
• Date: Wed, 10 Apr 2024 03:54:53 GMT
• Title: Sparse Points to Dense Clouds: Enhancing 3D Detection with Limited LiDAR Data
• Authors: Aakash Kumar, Chen Chen, Ajmal Mian, Neils Lobo, Mubarak Shah,
• Abstract summary: We propose a balanced approach that combines the advantages of monocular and point cloud-based 3D detection. Our method requires only a small number of 3D points, that can be obtained from a low-cost, low-resolution sensor. The accuracy of 3D detection improves by 20% compared to the state-of-the-art monocular detection methods.
• Score: 68.18735997052265
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D detection is a critical task that enables machines to identify and locate objects in three-dimensional space. It has a broad range of applications in several fields, including autonomous driving, robotics and augmented reality. Monocular 3D detection is attractive as it requires only a single camera, however, it lacks the accuracy and robustness required for real world applications. High resolution LiDAR on the other hand, can be expensive and lead to interference problems in heavy traffic given their active transmissions. We propose a balanced approach that combines the advantages of monocular and point cloud-based 3D detection. Our method requires only a small number of 3D points, that can be obtained from a low-cost, low-resolution sensor. Specifically, we use only 512 points, which is just 1% of a full LiDAR frame in the KITTI dataset. Our method reconstructs a complete 3D point cloud from this limited 3D information combined with a single image. The reconstructed 3D point cloud and corresponding image can be used by any multi-modal off-the-shelf detector for 3D object detection. By using the proposed network architecture with an off-the-shelf multi-modal 3D detector, the accuracy of 3D detection improves by 20% compared to the state-of-the-art monocular detection methods and 6% to 9% compare to the baseline multi-modal methods on KITTI and JackRabbot datasets.

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