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Shape Representation Of 3D Point Clouds

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코드

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

https://github.com/mlvlab/spotr

공식 코드

Self-positioning Point-based Transformer for Point Cloud Understanding

https://github.com/mmmmimic/diffconvnet

공식 코드

diffConv: Analyzing Irregular Point Clouds with an Irregular View

https://github.com/mohamedafham/crosspoint

공식 코드

CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding

https://github.com/mr-neko/jm3d

공식 코드

Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representation

https://github.com/mutianxu/GDANet

공식 코드

Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud

https://github.com/myavartanoo/PolyNet_PyTorch

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PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation

https://github.com/mys007/ecc

공식 코드

Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs

https://github.com/nsavinov/semantic3dnet

공식 코드

Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark

https://github.com/openrobotlab/pointllm

공식 코드

PointLLM: Empowering Large Language Models to Understand Point Clouds

https://github.com/orendv/learning_to_sample

공식 코드

Learning to Sample

https://github.com/pavlo-melnyk/tetrasphere

공식 코드

TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis

https://github.com/princeton-vl/SimpleView

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Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline

https://github.com/qizekun/ReCon

공식 코드

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

https://github.com/qizekun/ShapeLLM

공식 코드

ShapeLLM: Universal 3D Object Understanding for Embodied Interaction

https://github.com/ranahanocka/MeshCNN

공식 코드

MeshCNN: A Network with an Edge

https://github.com/raoyongming/PointGLR

공식 코드

Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds

https://github.com/rubenwiersma/deltaconv

공식 코드

DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds

https://github.com/runpeidong/act

공식 코드

Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?

https://github.com/saba99/classifying-objects-3d-point-clouds

공식 코드

Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approach

https://github.com/said-ohamouddou/stft-kan-litedgcnn

공식 코드

Introducing the Short-Time Fourier Kolmogorov Arnold Network: A Dynamic Graph CNN Approach for Tree Species Classification in 3D Point Clouds

https://github.com/salesforce/ulip

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ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding

https://github.com/sgi-2023/3d-building-classification

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DC3DO: Diffusion Classifier for 3D Objects

https://github.com/shichengchen/multiviewdataset

공식 코드

Local and Global Point Cloud Reconstruction for 3D Hand Pose Estimation

https://github.com/shinke-li/pointcvar

공식 코드

Risk-optimized Outlier Removal for Robust 3D Point Cloud Classification

https://github.com/simingyan/iae

공식 코드

Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning

https://github.com/sitzikbs/3DmFV-Net

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3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks

https://github.com/skyworkai/pointcloudmamba

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Point Cloud Mamba: Point Cloud Learning via State Space Model

https://github.com/tangyuan96/minigpt-3d

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MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors

https://github.com/tejasanvekar/gpr-net

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GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot Learning

https://github.com/tensorflow/tensor2tensor

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Attention Is All You Need

https://github.com/tiangexiang/CurveNet

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Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis

https://github.com/trlou/hit-adv

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Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Clouds

https://github.com/tyhuang0428/CLIP2Point

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CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training

https://github.com/ucsc-vlaa/mixcon3d

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Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training

https://github.com/val-iisc/3d-lmnet

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3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image

https://github.com/val-iisc/3d-psrnet

공식 코드

3D-PSRNet: Part Segmented 3D Point Cloud Reconstruction From a Single Image

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