Point cloud completion is a generation and estimation issue derived from the partial point
clouds, which plays a vital role in the applications of 3D computer vision. The progress of …

The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of
deep learning (DL). However, the latter faces various issues, including the lack of data or …

We present a new domain adaptive self-training pipeline, named ST3D, for unsupervised
domain adaptation on 3D object detection from point clouds. First, we pre-train the 3D …

Training a 3D scene understanding model requires complicated human annotations, which
are laborious to collect and result in a model only encoding close-set object semantics. In …

Self-supervised image backbones can be used to address complex 2D tasks (eg semantic
segmentation object discovery) very efficiently and with little or no downstream supervision …

The task of point cloud segmentation, comprising semantic, instance, and panoptic
segmentation, has been mainly tackled by designing task-specific network architectures …

Abstract Knowledge transfer from synthetic to real data has been widely studied to mitigate
data annotation constraints in various computer vision tasks such as semantic segmentation …

State-of-the-art methods for driving-scene LiDAR-based perception (including point cloud
semantic segmentation, panoptic segmentation and 3D detection, etc.) often project the …

Deep learning-based 3D object detection has achieved unprecedented success with the
advent of large-scale autonomous driving datasets. However, drastic performance …

In this paper, we propose the LiDAR Distillation to bridge the domain gap induced by
different LiDAR beams for 3D object detection. In many real-world applications, the LiDAR …