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Scene flow represents the 3D motion of each point in the scene, whichexplicitly describes the distance and the direction of each point's movement.Scene flow estimation is used in various applications such as autonomousdriving fields, activity recognition, and virtual reality fields. As it ischallenging to annotate scene flow with ground truth for real-world data, thisleaves no real-world dataset available to provide a large amount of data withground truth for scene flow estimation. Therefore, many works use synthesizeddata to pre-train their network and real-world LiDAR data to finetune. Unlikethe previous unsupervised learning of scene flow in point clouds, we propose touse odometry information to assist the unsupervised learning of scene flow anduse real-world LiDAR data to train our network. Supervised odometry providesmore accurate shared cost volume for scene flow. In addition, the proposednetwork has mask-weighted warp layers to get a more accurate predicted pointcloud. The warp operation means applying an estimated pose transformation orscene flow to a source point cloud to obtain a predicted point cloud and is thekey to refining scene flow from coarse to fine. When performing warpoperations, the points in different states use different weights for the posetransformation and scene flow transformation. We classify the states of pointsas static, dynamic, and occluded, where the static masks are used to dividestatic and dynamic points, and the occlusion masks are used to divide occludedpoints. The mask-weighted warp layer indicates that static masks and occlusionmasks are used as weights when performing warp operations. Our designs areproved to be effective in ablation experiments. The experiment results show thepromising prospect of an odometry-assisted unsupervised learning method for 3Dscene flow in real-world data.

Unsupervised Learning of 3D Scene Flow with 3D Odometry Assistance