CamLiFlow: Bidirectional Camera-LiDAR Fusion for Joint Optical Flow and Scene Flow Estimation

• URL: http://arxiv.org/abs/2111.10502v1
• Date: Sat, 20 Nov 2021 02:58:38 GMT
• Title: CamLiFlow: Bidirectional Camera-LiDAR Fusion for Joint Optical Flow and Scene Flow Estimation
• Authors: Haisong Liu, Tao Lu, Yihui Xu, Jia Liu, Wenjie Li, Lijun Chen
• Abstract summary: We study the problem of jointly estimating the optical flow and scene flow from synchronized 2D and 3D data. To address the problem, we propose a novel end-to-end framework, called CamLiFlow. Our method ranks 1st on the KITTI Scene Flow benchmark, outperforming the previous art with 1/7 parameters.
• Score: 15.98323974821097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study the problem of jointly estimating the optical flow and scene flow from synchronized 2D and 3D data. Previous methods either employ a complex pipeline which splits the joint task into independent stages, or fuse 2D and 3D information in an ``early-fusion'' or ``late-fusion'' manner. Such one-size-fits-all approaches suffer from a dilemma of failing to fully utilize the characteristic of each modality or to maximize the inter-modality complementarity. To address the problem, we propose a novel end-to-end framework, called CamLiFlow. It consists of 2D and 3D branches with multiple bidirectional connections between them in specific layers. Different from previous work, we apply a point-based 3D branch to better extract the geometric features and design a symmetric learnable operator to fuse dense image features and sparse point features. We also propose a transformation for point clouds to solve the non-linear issue of 3D-2D projection. Experiments show that CamLiFlow achieves better performance with fewer parameters. Our method ranks 1st on the KITTI Scene Flow benchmark, outperforming the previous art with 1/7 parameters. Code will be made available.

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