Low-resolution point clouds are challenging for object detection methods due to their sparsity. Densifying the present point cloud by concatenating it with its predecessors is a popular solution to this challenge. Such concatenation is possible thanks to the removal of ego vehicle motion using its odometry. This method is called Ego Motion Compensation (EMC). Thanks to the added points, EMC significantly improves the performance of single-frame detectors. However, it suffers from the shadow effect that manifests in dynamic objects’ points scattering along their trajectories. This effect results in a misalignment between feature maps and objects’ locations, thus limiting performance improvement to stationary and slow-moving objects only. Scene flow allows aligning point clouds in 3D space, thus naturally resolving the misalignment in feature spaces. By observing that scene flow computation shares several components with 3D object detection pipelines, we develop a plug-in module that enables single-frame detectors to compute scene flow to rectify their Bird-Eye View representation. Experiments on the NuScenes dataset show that our module leads to a significant increase (up to 16%) in the Average Precision of large vehicles, which interestingly demonstrates the most severe shadow effect.
Aligning Bird-Eye View Representation of Point Cloud Sequences using Scene Flow
2023-06-04
1875295 byte
Conference paper
Electronic Resource
English
REPRESENTATION LEARNING FOR OBJECT DETECTION FROM UNLABELED POINT CLOUD SEQUENCES
European Patent Office | 2024
|Aligning Non-Overlapping Sequences
British Library Online Contents | 2002
|Multi-view Scene Flow Estimation: A View Centered Variational Approach
British Library Online Contents | 2013
|