Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

Image instance segmentation involves labeling pixels of images with classes and instances,
which is one of the pivotal technologies in many domains, such as natural scenes …

Existing state-of-the-art 3D instance segmentation methods perform semantic segmentation
followed by grou**. The hard predictions are made when performing semantic …

We propose 3DETR, an end-to-end Transformer based object detection model for 3D point
clouds. Compared to existing detection methods that employ a number of 3D-specific …

Casting semantic segmentation of outdoor LiDAR point clouds as a 2D problem, eg, via
range projection, is an effective and popular approach. These projection-based methods …

State-of-the-art methods for large-scale driving-scene LiDAR segmentation often project the
point clouds to 2D space and then process them via 2D convolution. Although this …

Instance segmentation is an important task for scene understanding. Compared to the fully-
developed 2D, 3D instance segmentation for point clouds have much room to improve. In …

Instance segmentation on point clouds is a fundamental task in 3D scene perception. In this
work, we propose a concise clustering-based framework named HAIS, which makes full use …

Pretraining on large labeled datasets is a prerequisite to achieve good performance in many
computer vision tasks like image recognition, video understanding etc. However, pretraining …

LiDAR point cloud analysis is a core task for 3D computer vision, especially for autonomous
driving. However, due to the severe sparsity and noise interference in the single sweep …