Small, Versatile and Mighty: A Range-View Perception Framework
Qiang Meng Xiao Wang JiaBao Wang Liujiang Yan Ke Wang
Despite its compactness and information integrity, the range view
representation of LiDAR data rarely occurs as the first choice for 3D
perception tasks. In this work, we further push the envelop of the range-view
representation with a novel multi-task framework, achieving unprecedented 3D
detection performances. Our proposed Small, Versatile, and Mighty (SVM) network
utilizes a pure convolutional architecture to fully unleash the efficiency and
multi-tasking potentials of the range view representation. To boost detection
performances, we first propose a range-view specific Perspective Centric Label
Assignment (PCLA) strategy, and a novel View Adaptive Regression (VAR) module
to further refine hard-to-predict box properties. In addition, our framework
seamlessly integrates semantic segmentation and panoptic segmentation tasks for
the LiDAR point cloud, without extra modules. Among range-view-based methods,
our model achieves new state-of-the-art detection performances on the Waymo
Open Dataset. Especially, over 10 mAP improvement over convolutional
counterparts can be obtained on the vehicle class. Our presented results for
other tasks further reveal the multi-task capabilities of the proposed small
but mighty framework.