SOTAVerified

3D Point Cloud Linear Classification

Training a linear classifier(e.g. SVM) on the embeddings/representations of 3D point clouds. The embeddings/representations are usually trained in an unsupervised manner.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 21 papers

TitleStatusHype
AdaCrossNet: Adaptive Dynamic Loss Weighting for Cross-Modal Contrastive Point Cloud LearningCode0
Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point CloudCode1
ShapeLLM: Universal 3D Object Understanding for Embodied InteractionCode3
CrossMoCo: Multi-modal Momentum Contrastive Learning for Point CloudCode0
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative PretrainingCode1
Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked AutoencodersCode2
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-trainingCode2
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud UnderstandingCode2
Implicit Autoencoder for Point-Cloud Self-Supervised Representation LearningCode1
Progressive Seed Generation Auto-encoder for Unsupervised Point Cloud Learning—0
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All datasetsModelNet40ScanObjectNN

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CrossMoCoOverall Accuracy86.06—Unverified
2AdaCrossNetOverall Accuracy82.1—Unverified
3CrossPointOverall Accuracy81.7—Unverified
4OcCoOverall Accuracy78.3—Unverified