Point Cloud Segmentation

41 papers with code • 0 benchmarks • 0 datasets

3D point cloud segmentation is the process of classifying point clouds into multiple homogeneous regions, the points in the same region will have the same properties. The segmentation is challenging because of high redundancy, uneven sampling density, and lack explicit structure of point cloud data. This problem has many applications in robotics such as intelligent vehicles, autonomous mapping and navigation.

Source: 3D point cloud segmentation: A survey

Most implemented papers

PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation

MVIG-SJTU/pointSIFT 2 Jul 2018

Recently, 3D understanding research sheds light on extracting features from point cloud directly, which requires effective shape pattern description of point clouds.

Fast 3D Line Segment Detection From Unorganized Point Cloud

xiaohulugo/3DLineDetection 8 Jan 2019

This paper presents a very simple but efficient algorithm for 3D line segment detection from large scale unorganized point cloud.

Deep Learning for 3D Point Clouds: A Survey

QingyongHu/SoTA-Point-Cloud 27 Dec 2019

To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds.

LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices

AIS-Bonn/lattice_net 12 Dec 2019

Deep convolutional neural networks (CNNs) have shown outstanding performance in the task of semantically segmenting images.

SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation

chenfengxu714/SqueezeSegV3 ECCV 2020

Using standard convolutions to process such LiDAR images is problematic, as convolution filters pick up local features that are only active in specific regions in the image.

SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation

AnTao97/SegGroup 18 Dec 2020

Most existing point cloud instance and semantic segmentation methods rely heavily on strong supervision signals, which require point-level labels for every point in the scene.

Stratified Transformer for 3D Point Cloud Segmentation

dvlab-research/stratified-transformer 28 Mar 2022

In this paper, we propose Stratified Transformer that is able to capture long-range contexts and demonstrates strong generalization ability and high performance.

Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds

viscom-ulm/MCCNN 5 Jun 2018

We propose an efficient and effective method to learn convolutions for non-uniformly sampled point clouds, as they are obtained with modern acquisition techniques.

cilantro: A Lean, Versatile, and Efficient Library for Point Cloud Data Processing

kzampog/cilantro 1 Jul 2018

We introduce cilantro, an open-source C++ library for geometric and general-purpose point cloud data processing.

SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud

xuanyuzhou98/SqueezeSegV2 22 Sep 2018

When training our new model on synthetic data using the proposed domain adaptation pipeline, we nearly double test accuracy on real-world data, from 29. 0% to 57. 4%.