In this paper, we study the problem of 3D object segmentation from raw point clouds. Unlike
all existing methods which usually require a large amount of human annotations for full …

Instance segmentation on 3D point clouds (3DIS) is a longstanding challenge in computer
vision, where state-of-the-art methods are mainly based on full supervision. As annotating …

3D Instance segmentation is a fundamental task in computer vision. Effective segmentation
plays an important role in robotic tasks, augmented reality, autonomous driving, etc. With the …

Abstract 3D instance segmentation plays a predominant role in environment perception of
robotics and augmented reality. Many deep learning based methods have been presented …

In this paper, we propose a novel joint instance and semantic segmentation approach,
which is called JSNet, in order to address the instance and semantic segmentation of 3D …

Previous top-performing approaches for point cloud instance segmentation involve a bottom-
up strategy, which often includes inefficient operations or complex pipelines, such as …

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

In this paper, we propose a novel method named JSPNet, to segment 3D point cloud in
semantic and instance simultaneously. First, we analyze the problem in addressing joint …

Instance segmentation on point clouds is crucially important for 3D scene understanding.
Most SOTAs adopt distance clustering, which is typically effective but does not perform well …

Point cloud semantic segmentation plays an essential role in autonomous driving, providing
vital information about drivable surfaces and nearby objects that can aid higher level tasks …