LIDAR Semantic Segmentation

30 papers with code • 3 benchmarks • 6 datasets

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Most implemented papers

KPConv: Flexible and Deformable Convolution for Point Clouds

HuguesTHOMAS/KPConv ICCV 2019

Furthermore, these locations are continuous in space and can be learned by the network.

RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

QingyongHu/RandLA-Net CVPR 2020

We study the problem of efficient semantic segmentation for large-scale 3D point clouds.

Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation

xinge008/Cylinder3D 4 Aug 2020

A straightforward solution to tackle the issue of 3D-to-2D projection is to keep the 3D representation and process the points in the 3D space.

Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation

xinge008/Cylinder3D CVPR 2021

However, we found that in the outdoor point cloud, the improvement obtained in this way is quite limited.

LaserMix for Semi-Supervised LiDAR Semantic Segmentation

ldkong1205/LaserMix 30 Jun 2022

Densely annotating LiDAR point clouds is costly, which restrains the scalability of fully-supervised learning methods.

PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds

xiaoaoran/polarmix 30 Jul 2022

The first is scene-level swapping which exchanges point cloud sectors of two LiDAR scans that are cut along the azimuth axis.

ConvPoint: Continuous Convolutions for Point Cloud Processing

aboulch/ConvPoint 4 Apr 2019

Point clouds are unstructured and unordered data, as opposed to images.

3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation

Shathe/3D-MiniNet 25 Feb 2020

Fast and efficient semantic segmentation methods are needed to match the strong computational and temporal restrictions of many of these real-world applications.

FKAConv: Feature-Kernel Alignment for Point Cloud Convolution

valeoai/FKAConv 9 Apr 2020

Recent state-of-the-art methods for point cloud processing are based on the notion of point convolution, for which several approaches have been proposed.