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3D Semantic Segmentation On Scannet200

Metrics

test mIoU

val mIoU

Results

Performance results of various models on this benchmark

Model Name	test mIoU	val mIoU	Paper Title	Repository
DITR	44.9	41.2	DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation	-
PPT+SparseUNet	33.2	31.9	Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training	-
Pamba	37.1	36.3	Pamba: Enhancing Global Interaction in Point Clouds via State Space Model	-
OctFormer	32.5	32.6	OctFormer: Octree-based Transformers for 3D Point Clouds	-
PonderV2 + SparseUNet	34.6	32.3	PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm	-
OneFormer3D	-	30.1	OneFormer3D: One Transformer for Unified Point Cloud Segmentation	-
OA-CNNs	32.3	33.3	OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentation	-
LSK3DNet	-	33.1	LSK3DNet: Towards Effective and Efficient 3D Perception with Large Sparse Kernels	-
CSC	24.9	26.4	Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts	-
MinkUNet	25.3	25.0	4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks	-
ODIN	36.8	40.5	ODIN: A Single Model for 2D and 3D Segmentation	-
Sonata + PTv3	-	36.8	Sonata: Self-Supervised Learning of Reliable Point Representations
BFANet	36.0	37.3	BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysis	-
PTv3 + PPT	39.3	36.0	Point Transformer V3: Simpler, Faster, Stronger	-
LGround	27.2	28.8	Language-Grounded Indoor 3D Semantic Segmentation in the Wild	-
PTv3 ArKitLabelmaker	41.4	40.3	ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding	-

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3D Semantic Segmentation On Scannet200 | SOTA | HyperAI