Dense Voxel Fusion for 3D Object Detection
- URL: http://arxiv.org/abs/2203.00871v1
- Date: Wed, 2 Mar 2022 04:51:31 GMT
- Title: Dense Voxel Fusion for 3D Object Detection
- Authors: Anas Mahmoud, Jordan S. K. Hu and Steven L. Waslander
- Abstract summary: Voxel Fusion (DVF) is a sequential fusion method that generates multi-scale dense voxel feature representations.
We train directly with ground truth 2D bounding box labels, avoiding noisy, detector-specific, 2D predictions.
We show that our proposed multi-modal training strategy results in better generalization compared to training using erroneous 2D predictions.
- Score: 10.717415797194896
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Camera and LiDAR sensor modalities provide complementary appearance and
geometric information useful for detecting 3D objects for autonomous vehicle
applications. However, current fusion models underperform state-of-art
LiDAR-only methods on 3D object detection benchmarks. Our proposed solution,
Dense Voxel Fusion (DVF) is a sequential fusion method that generates
multi-scale multi-modal dense voxel feature representations, improving
expressiveness in low point density regions. To enhance multi-modal learning,
we train directly with ground truth 2D bounding box labels, avoiding noisy,
detector-specific, 2D predictions. Additionally, we use LiDAR ground truth
sampling to simulate missed 2D detections and to accelerate training
convergence. Both DVF and the multi-modal training approaches can be applied to
any voxel-based LiDAR backbone without introducing additional learnable
parameters. DVF outperforms existing sparse fusion detectors, ranking $1^{st}$
among all published fusion methods on KITTI's 3D car detection benchmark at the
time of submission and significantly improves 3D vehicle detection performance
of voxel-based methods on the Waymo Open Dataset. We also show that our
proposed multi-modal training strategy results in better generalization
compared to training using erroneous 2D predictions.
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