3D Point Cloud Linear Classification

15 papers with code • 2 benchmarks • 2 datasets

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.


Use these libraries to find 3D Point Cloud Linear Classification models and implementations
3 papers

Most implemented papers

FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation

AnTao97/UnsupervisedPointCloudReconstruction CVPR 2018

Recent deep networks that directly handle points in a point set, e. g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and segmentation.

SO-Net: Self-Organizing Network for Point Cloud Analysis

lijx10/SO-Net CVPR 2018

This paper presents SO-Net, a permutation invariant architecture for deep learning with orderless point clouds.

Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training

zrrskywalker/point-m2ae 28 May 2022

By fine-tuning on downstream tasks, Point-M2AE achieves 86. 43% accuracy on ScanObjectNN, +3. 36% to the second-best, and largely benefits the few-shot classification, part segmentation and 3D object detection with the hierarchical pre-training scheme.

Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining

qizekun/ReCon 5 Feb 2023

This motivates us to learn 3D representations by sharing the merits of both paradigms, which is non-trivial due to the pattern difference between the two paradigms.

ShapeLLM: Universal 3D Object Understanding for Embodied Interaction

qizekun/ShapeLLM 27 Feb 2024

This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages.

Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling

lulutang0608/Point-BERT CVPR 2022

Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers.

Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders

zrrskywalker/i2p-mae CVPR 2023

Pre-training by numerous image data has become de-facto for robust 2D representations.

Self-supervised Learning of Point Clouds via Orientation Estimation

OmidPoursaeed/Self_supervised_Learning_Point_Clouds 1 Aug 2020

A point cloud can be rotated in infinitely many ways, which provides a rich label-free source for self-supervision.

Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination

Microsoft/O-CNN 3 Aug 2020

Although unsupervised feature learning has demonstrated its advantages to reducing the workload of data labeling and network design in many fields, existing unsupervised 3D learning methods still cannot offer a generic network for various shape analysis tasks with competitive performance to supervised methods.