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Semantic Segmentation On S3Dis Area5

Metrics

Number of params
mAcc
mIoU
oAcc

Results

Performance results of various models on this benchmark

Paper Title
Sonata + PTv3-81.676.093.0Sonata: Self-Supervised Learning of Reliable Point Representations
OmniVec--75.9-OmniVec: Learning robust representations with cross modal sharing
PTv3 + PPT-80.174.792.0Point Transformer V3: Simpler, Faster, Stronger
Swin3D-LN/A80.574.592.7Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding
DITR--74.1-DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation
DeLA7.0M80.074.192.2Decoupled Local Aggregation for Point Cloud Learning
Ours-80.273.693.0Beyond local patches: Preserving global–local interactions by enhancing self-attention via 3D point cloud tokenization
Pamba--73.5-Pamba: Enhancing Global Interaction in Point Clouds via State Space Model
ConDaFormer-78.973.592.4ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding
LPFP(Point Transformer*)31.2M78.773.592.0A Large-Scale Network Construction and Lightweighting Method for Point Cloud Semantic Segmentation
KPConvX-L-78.773.591.7KPConvX: Modernizing Kernel Point Convolution with Kernel Attention
SPG(PTv2)-79.573.391.9Subspace Prototype Guidance for Mitigating Class Imbalance in Point Cloud Semantic Segmentation
PointHR-78.773.291.8PointHR: Exploring High-Resolution Architectures for 3D Point Cloud Segmentation
PonderV2 + SparseUNet-79.073.292.2PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
PPT + SparseUNetN/A78.272.791.5Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training
PTv2N/A78.072.691.6Point Transformer V2: Grouped Vector Attention and Partition-based Pooling
PT + ERDA--72.6--
SAT (FAT)N/A78.872.6-SAT: Size-Aware Transformer for 3D Point Cloud Semantic Segmentation
PointVector-XL-78.172.391PointVector: A Vector Representation In Point Cloud Analysis
WindowNorm+StratifiedTransformerN/A78.272.291.4Window Normalization: Enhancing Point Cloud Understanding by Unifying Inconsistent Point Densities
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