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3D Object Recognition

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하위 태스크: 0
평가표: 4
보존된 평가 기록: 9

3D object recognition is the task of recognising objects from 3D data.

Note that there are related tasks you can look at, such as 3D Object Detection which have more leaderboards.

<span style="color:grey; opacity: 0.6">(Image credit: Look Further to Recognize Better)</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AlonLahav/MeshWalker

공식 코드

MeshWalker: Deep Mesh Understanding by Random Walks

https://github.com/DLR-RM/BlenderProc

공식 코드

BlenderProc

https://github.com/RosettaWYzhang/AdaPC

공식 코드

On Automatic Data Augmentation for 3D Point Cloud Classification

https://github.com/WXinlong/ForeSeE

공식 코드

Task-Aware Monocular Depth Estimation for 3D Object Detection

https://github.com/btgraham/SparseConvNet

공식 코드

Sparse 3D convolutional neural networks

https://github.com/dkoguciuk/ensemble_learning_for_point_clouds

공식 코드

3D Object Recognition with Ensemble Learning --- A Study of Point Cloud-Based Deep Learning Models

https://github.com/explain3d/exp-one-point-atk-pc

공식 코드

Explainability-Aware One Point Attack for Point Cloud Neural Networks

https://github.com/facebookresearch/omni3d

공식 코드

Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild

https://github.com/fate3439/dscnn

공식 코드

Dominant Set Clustering and Pooling for Multi-View 3D Object Recognition

https://github.com/github-songsong/fine-grained-pointcloud-object-dataset

공식 코드

Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN Models

https://github.com/hysonlab/pointdistill

공식 코드

Topology-Guided Knowledge Distillation for Efficient Point Cloud Processing

https://github.com/jinfengx/sasep

공식 코드

SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds

https://github.com/krishkribo/3d_gdm-rson

공식 코드

Lifelong 3D Object Recognition and Grasp Synthesis Using Dual Memory Recurrent Self-Organization Networks

https://github.com/lorenmt/vsl

공식 코드

Learning a Hierarchical Latent-Variable Model of 3D Shapes

https://github.com/megayeye/triangle-net

공식 코드

Triangle-Net: Towards Robustness in Point Cloud Learning

https://github.com/memoatwit/universalperturbation

공식 코드

One Noise to Rule Them All: Multi-View Adversarial Attacks with Universal Perturbation

https://github.com/shanshuo/MVT

공식 코드

MVT: Multi-view Vision Transformer for 3D Object Recognition

https://github.com/shanshuo/R2-MLP

공식 코드

R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition

https://github.com/snehaputul/allmatch

공식 코드

Improving 3D Semi-supervised Learning by Effectively Utilizing All Unlabelled Data

https://github.com/tteepe/tracktacular

공식 코드

Lifting Multi-View Detection and Tracking to the Bird's Eye View

https://github.com/yangyanli/FPNN

공식 코드

FPNN: Field Probing Neural Networks for 3D Data

https://github.com/yzhou359/3DIndoor-SceneGraphNet

공식 코드

SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation

https://github.com/zubair-irshad/shapo

공식 코드

FSD: Fast Self-Supervised Single RGB-D to Categorical 3D Objects

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.