3D Semantic Segmentation
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Benchmarks
SemanticKITTI
ScanNet200
DALES
KITTI-360
ScanNet++
SensatUrban
Toronto-3D
PartNet
S3DIS
STPLS3D
ScribbleKITTI
RELLIS-3D Dataset
WildScenes
OpenTrench3D
nuScenes
Hypersim
Waymo Open Dataset
3D Platelet EM
ECLAIR
Most implemented
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Point Transformer
Dynamic Graph CNN for Learning on Point Clouds
KPConv: Flexible and Deformable Convolution for Point Clouds
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
Papers
VLRC: Vision-Language Reprojection Consistency as a scalable signal for better feed-forward 3D pretraining
Feed-forward 3D models are commonly trained using either expensive geometric supervision or self-supervised photometric objectives, both of which provide incomplete learning signals. We introduce Vision-Language Reprojec…
3D Semantic SegmentationScene Understanding3D ReconstructionPrivacy-Preserving Depth-Only Open-Vocabulary 3D Semantic Segmentation Via Uncertainty-Guided Test-Time Optimization
Privacy-preserving perception is a critical requirement for deploying 3D scene understanding systems in real-world indoor environments, yet it remains underexplored in open-vocabulary 3D semantic segmentation. Existing m…
3D Semantic SegmentationScene UnderstandingHeterogeneous and Adept Snapshot Distillation for 3D Semantic Segmentation
Multi-modal fusion and multi-model ensembling are prevalent in enhancing the performance of 3D semantic segmentation. Despite the impressive performance, these methods either rely on auxiliary input signals or suffer fro…
3D Semantic SegmentationKnowledge DistillationPoint CloudsGIBLy: Improving 3D Semantic Segmentation through an Architecture-Agnostic Lightweight Geometric Inductive Bias Layer
In 3D scene understanding, deep learning models rely on large models and extensive training to capture basic geometric structures that are present in the 3D data. However, existing methods lack explicit mechanisms to inc…
3D Semantic SegmentationScene UnderstandingCollaborative Learning for Semi-Supervised LiDAR Semantic Segmentation
Annotating large-scale LiDAR point clouds for 3D semantic segmentation is costly and time-consuming, which motivates the use of semi-supervised learning (SemiSL). Standard LiDAR SemiSL methods typically adopt a two-step …
LIDAR Semantic Segmentation3D Semantic SegmentationPoint CloudsOPTNet: Ordering Point Transformer Network for Post-disaster 3D Semantic Segmentation
Post-disaster damage assessment requires rapid and accurate semantic segmentation of 3D point clouds to identify critical infrastructure such as damaged buildings and roads. Early Point Transformers (e.g., PTv1, PTv2) re…
3D Semantic SegmentationPoint Clouds