3D Semantic Segmentation
168 papers with code • 14 benchmarks • 31 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.
Benchmarks
These leaderboards are used to track progress in 3D Semantic Segmentation
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Libraries
Use these libraries to find 3D Semantic Segmentation models and implementationsDatasets
nuScenes
SemanticKITTI
S3DIS
KITTI-360
PartNet
SemanticPOSS
AMOS
RELLIS-3D
STPLS3D
nuScenes-C
Subtasks
Most implemented papers
PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding
yangyanli/PointCNN
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CVPR 2019
We present PartNet: a consistent, large-scale dataset of 3D objects annotated with fine-grained, instance-level, and hierarchical 3D part information.
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR.
SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving
In this paper, we introduce SalsaNext for the uncertainty-aware semantic segmentation of a full 3D LiDAR point cloud in real-time.
Point-Voxel CNN for Efficient 3D Deep Learning
mit-han-lab/pvcnn
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NeurIPS 2019
The computation cost and memory footprints of the voxel-based models grow cubically with the input resolution, making it memory-prohibitive to scale up the resolution.
DeepGCNs: Making GCNs Go as Deep as CNNs
This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs.
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge
The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) which sought to address these issues and stimulate progress on this automatic segmentation problem.
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
edwardzhou130/PolarSeg
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CVPR 2020
The need for fine-grained perception in autonomous driving systems has resulted in recently increased research on online semantic segmentation of single-scan LiDAR.
Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images
kleinzcy/SASSnet
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Semi-supervised learning has attracted much attention in medical image segmentation due to challenges in acquiring pixel-wise image annotations, which is a crucial step for building high-performance deep learning methods.
STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset
Specifically, we introduce a synthetic aerial photogrammetry point clouds generation pipeline that takes full advantage of open geospatial data sources and off-the-shelf commercial packages.
Associatively Segmenting Instances and Semantics in Point Clouds
A 3D point cloud describes the real scene precisely and intuitively. To date how to segment diversified elements in such an informative 3D scene is rarely discussed.
