HyperAI
AccueilActualitésArticles de recherche récentsTutorielsEnsembles de donnéesWikiSOTAModèles LLMClassement GPUÉvénements
Recherche
À propos
HyperAI

3D Semantic Segmentation On Scannet200

Métriques

test mIoU
val mIoU

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
test mIoU
val mIoU
Paper TitleRepository
DITR44.941.2--
PPT+SparseUNet33.231.9Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training
Pamba37.136.3Pamba: Enhancing Global Interaction in Point Clouds via State Space Model-
OctFormer32.532.6OctFormer: Octree-based Transformers for 3D Point Clouds
PonderV2 + SparseUNet34.632.3PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
OneFormer3D-30.1OneFormer3D: One Transformer for Unified Point Cloud Segmentation
OA-CNNs32.333.3OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentation
LSK3DNet-33.1LSK3DNet: Towards Effective and Efficient 3D Perception with Large Sparse Kernels
CSC24.926.4Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts
MinkUNet25.325.04D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks
ODIN36.840.5ODIN: A Single Model for 2D and 3D Segmentation
Sonata + PTv3-36.8--
BFANet36.037.3--
PTv3 + PPT39.336.0Point Transformer V3: Simpler, Faster, Stronger
LGround27.228.8Language-Grounded Indoor 3D Semantic Segmentation in the Wild
PTv3 ArKitLabelmaker41.440.3ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding
0 of 16 row(s) selected.