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computer-vision-5

3D Point Cloud Linear Classification

3260 papers • 126 benchmarks • 313 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.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in 3d-point-cloud-linear-classification-5

Trend

Dataset

Best Model

Actions

ModelNet40

ModelNet40

ScanObjectNN

ScanObjectNN

Libraries

i

Use these libraries to find 3d-point-cloud-linear-classification-5 models and implementations

3 papers 91

Datasets

ModelNet

ScanObjectNN

Subtasks

No subtasks available.

Most implemented papers

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

Jiajun Wu, J. Tenenbaum, Tianfan Xue, Chengkai Zhang, Bill Freeman•Sun Oct 23 2016

A novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convolutional networks and generative adversarial nets, and a powerful 3D shape descriptor which has wide applications in 3D object recognition.

2085

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

0

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

Gim Hee Lee, Jiaxin Li, Ben M. Chen•Sun Mar 11 2018

The proposed SO-Net, a permutation invariant architecture for deep learning with orderless point clouds, demonstrates performance that is similar with or better than state-of-the-art approaches in recognition tasks such as point cloud reconstruction, classification, object part segmentation and shape retrieval.

990 0

FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation

Yiru Shen, Chen Feng, Yaoqing Yang, Dong Tian•Mon Dec 18 2017

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. In this work, a novel end-to-end deep auto-encoder is proposed to address unsupervised learning challenges on point clouds. On the encoder side, a graph-based enhancement is enforced to promote local structures on top of PointNet. Then, a novel folding-based decoder deforms a canonical 2D grid onto the underlying 3D object surface of a point cloud, achieving low reconstruction errors even for objects with delicate structures. The proposed decoder only uses about 7% parameters of a decoder with fully-connected neural networks, yet leads to a more discriminative representation that achieves higher linear SVM classification accuracy than the benchmark. In addition, the proposed decoder structure is shown, in theory, to be a generic architecture that is able to reconstruct an arbitrary point cloud from a 2D grid. Our code is available at http://www.merl.com/research/license#FoldingNet

1326 0

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

Hongsheng Li, Renrui Zhang, Peng Gao, Ziyu Guo, Rongyao Fang, Bingyan Zhao, Dong Wang, Y. Qiao•Fri May 27 2022

Point-M2AE is proposed, a strong Multi-scale MAE pre-training framework for hierarchical self-supervised learning of 3D point clouds that modifications the encoder and decoder into pyramid architectures to progressively model spatial geometries and capture both fine-grained and high-level semantics of3D shapes.

354 0

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

Zekun Qi, Runpei Dong, Guo Fan, Zheng Ge, Xiangyu Zhang, Kaisheng Ma, Li Yi•Sat Feb 04 2023

ReCon is trained to learn from both generative modeling teachers and single/cross-modal contrastive teachers through ensemble distillation, where the generative student guides the contrastive student.

192 0

ShapeLLM: Universal 3D Object Understanding for Embodied Interaction

Zekun Qi, Runpei Dong, Shaochen Zhang, Haoran Geng, Chunrui Han, Zheng Ge, Li Yi, Kaisheng Ma•Mon Feb 26 2024

ShapeLLM is the first 3D Multimodal Large Language Model designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages, built upon an improved 3D encoder by extending ReCon to ReCon++ that benefits from multi-view image distillation for enhanced geometry understanding.

114 0

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

Jiwen Lu, Tiejun Huang, Jie Zhou, Xumin Yu, Lulu Tang, Yongming Rao•Sun Nov 28 2021

Point-BERT, a new paradigm for learning Transformers to generalize the concept of BERT to 3D point cloud, is presented and it is shown that a pure Transformer architecture attains 93.8% accuracy on ModelNet40 and 83.1% accuracy in the hardest setting of ScanObjectNN, surpassing carefully designed point cloud models with much fewer hand-made designs.

947 0

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

Hongsheng Li, Renrui Zhang, Peng Gao, Y. Qiao, Liuhui Wang•Mon Dec 12 2022

This paper proposes an alternative to obtain superior 3D representations from 2D pre-trained models via Image-to-Point Masked Autoencoders, named as I2P-MAE, which leverages the well learned 2D knowledge to guide 3D masked autoencoding, which reconstructs the masked point tokens with an encoder-decoder architecture.

184 0

Self-Supervised Learning of Point Clouds via Orientation Estimation

Vladimir G. Kim, Omid Poursaeed, Tianxing Jiang, Quintessa Qiao, N. Xu•Fri Jul 31 2020

This paper leverages 3D self-supervision for learning downstream tasks on point clouds with fewer labels and demonstrates that its approach outperforms the state-of-the-art.

140 0

Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination

Zhirong Wu, Yang Liu, Xin Tong, Peng-Shuai Wang, Yuqi Yang, Qian-Fang Zou•Sun Aug 02 2020

An unsupervised method for learning a generic and efficient shape encoding network for different shape analysis tasks and surpasses existing supervised methods by a large margin for fine-grained shape segmentation on the PartNet dataset.

53 0

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3D Point Cloud Linear Classification | State-of-the-Art