3D Semantic Segmentation

153 papers with code • 12 benchmarks • 29 datasets

3D Semantic Segmentation is a computer vision task that involves dividing a 3D point cloud or 3D mesh into semantically meaningful parts or regions. The goal of 3D semantic segmentation is to identify and label different objects and parts within a 3D scene, which can be used for applications such as robotics, autonomous driving, and augmented reality.


Use these libraries to find 3D Semantic Segmentation models and implementations
11 papers
4 papers
2 papers
See all 6 libraries.

Most implemented papers

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

charlesq34/pointnet2 NeurIPS 2017

By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.

Point Transformer

Pointcept/Pointcept ICCV 2021

For example, on the challenging S3DIS dataset for large-scale semantic scene segmentation, the Point Transformer attains an mIoU of 70. 4% on Area 5, outperforming the strongest prior model by 3. 3 absolute percentage points and crossing the 70% mIoU threshold for the first time.

Dynamic Graph CNN for Learning on Point Clouds

WangYueFt/dgcnn 24 Jan 2018

Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices.

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.

4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks

StanfordVL/MinkowskiEngine CVPR 2019

To overcome challenges in the 4D space, we propose the hybrid kernel, a special case of the generalized sparse convolution, and the trilateral-stationary conditional random field that enforces spatio-temporal consistency in the 7D space-time-chroma space.

SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

BichenWuUCB/SqueezeSeg 19 Oct 2017

In this paper, we address semantic segmentation of road-objects from 3D LiDAR point clouds.

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.

PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding

yangyanli/PointCNN CVPR 2019

We present PartNet: a consistent, large-scale dataset of 3D objects annotated with fine-grained, instance-level, and hierarchical 3D part information.