3D Point Cloud Classification
128 papers with code • 5 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in 3D Point Cloud Classification
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Libraries
Use these libraries to find 3D Point Cloud Classification models and implementations
ModelNet
ScanObjectNN
IntrA
ModelNet40-C
Sydney Urban Objects
Teeth3DS
Subtasks
Latest papers
Risk-optimized Outlier Removal for Robust 3D Point Cloud Classification
With the growth of 3D sensing technology, deep learning system for 3D point clouds has become increasingly important, especially in applications like autonomous vehicles where safety is a primary concern.
ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformers
In this paper we delve into the properties of transformers, attained through self-supervision, in the point cloud domain.
CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud
snehaputul/CrossMoCo
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20th Conference on Robots and Vision (CRV) 2023
In this study, we introduce a novel selfsupervised method called CrossMoCo, which learns the representations of unlabelled point cloud data in a multi-modal setup that also utilizes the 2D rendered images of the point clouds.
Point-LGMask: Local and Global Contexts Embedding for Point Cloud Pre-training with Multi-Ratio Masking
TangYuan96/Point-LGMask
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IEEE Transactions on Multimedia 2023
In our work, we present Point-LGMask, a novel method to embed both local and global contexts with multi-ratio masking, which is quite effective for self-supervised feature learning of point clouds but is unfortunately ignored by existing pre-training works.
PointGPT: Auto-regressively Generative Pre-training from Point Clouds
CGuangyan-BIT/PointGPT
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NeurIPS 2023
Large language models (LLMs) based on the generative pre-training transformer (GPT) have demonstrated remarkable effectiveness across a diverse range of downstream tasks.
SUG: Single-dataset Unified Generalization for 3D Point Cloud Classification
In this paper, different from previous 2D DG works, we focus on the 3D DG problem and propose a Single-dataset Unified Generalization (SUG) framework that only leverages a single source dataset to alleviate the unforeseen domain differences faced by a well-trained source model.
ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding
salesforce/ulip
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It achieves a new SOTA of 50. 6% (top-1) on Objaverse-LVIS and 84. 7% (top-1) on ModelNet40 in zero-shot classification.
Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models
zyh16143998882/iccv23-idpt
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ICCV 2023
To conquer this limitation, we propose a novel Instance-aware Dynamic Prompt Tuning (IDPT) strategy for pre-trained point cloud models.
A Closer Look at Few-Shot 3D Point Cloud Classification
cgye96/a_closer_look_at_3dfsl
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In recent years, research on few-shot learning (FSL) has been fast-growing in the 2D image domain due to the less requirement for labeled training data and greater generalization for novel classes.
Self-positioning Point-based Transformer for Point Cloud Understanding
In this paper, we present a Self-Positioning point-based Transformer (SPoTr), which is designed to capture both local and global shape contexts with reduced complexity.
