3D Semantic Segmentation On Scannet200

Métriques

test mIoU

val mIoU

Résultats

Résultats de performance de divers modèles sur ce benchmark

			Paper Title
DITR	44.9	41.2	DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation
ODIN	36.8	40.5	ODIN: A Single Model for 2D and 3D Segmentation
PTv3 ArKitLabelmaker	41.4	40.3	ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding
BFANet	36.0	37.3	BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysis
Sonata + PTv3	-	36.8	Sonata: Self-Supervised Learning of Reliable Point Representations
Pamba	37.1	36.3	Pamba: Enhancing Global Interaction in Point Clouds via State Space Model
PTv3 + PPT	39.3	36.0	Point Transformer V3: Simpler, Faster, Stronger
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
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
PPT+SparseUNet	33.2	31.9	Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training
OneFormer3D	-	30.1	OneFormer3D: One Transformer for Unified Point Cloud Segmentation
LGround	27.2	28.8	Language-Grounded Indoor 3D Semantic Segmentation in the Wild
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

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3D Semantic Segmentation On Scannet200 | SOTA | HyperAI