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3D Object Classification
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3D Object Classification is the task of predicting the class of a 3D object point cloud. It is a voxel level prediction where each voxel is classified into a category. The popular benchmark for this task is the ModelNet dataset. The models for this task are usually evaluated with the Classification Accuracy metric.
Image: Sedaghat et al
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation
Efficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Net
PointNet with KAN versus PointNet with MLP for 3D Classification and Segmentation of Point Sets
MIRACLE 3D: Memory-efficient Integrated Robust Approach for Continual Learning on Point Clouds via Shape Model construction
Formula-Supervised Visual-Geometric Pre-training
Continual Learning in 3D Point Clouds: Employing Spectral Techniques for Exemplar Selection
GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning
PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders
DC3DO: Diffusion Classifier for 3D Objects
MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors
Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approach
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
Open-Pose 3D Zero-Shot Learning: Benchmark and Challenges
Improving Normalization with the James-Stein Estimator
Point Cloud Self-supervised Learning via 3D to Multi-view Masked Autoencoder
Fast Sparse 3D Convolution Network with VDB
Extending Multi-modal Contrastive Representations
Uni3D: Exploring Unified 3D Representation at Scale
Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following
PointLLM: Empowering Large Language Models to Understand Point Clouds
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease
3D-LLM: Injecting the 3D World into Large Language Models
CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud
Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation
MVImgNet: A Large-scale Dataset of Multi-view Images
Densely Connected $G$-invariant Deep Neural Networks with Signed Permutation Representations
Unsupervised 3D Object Learning through Neuron Activity aware Plasticity
PaRot: Patch-Wise Rotation-Invariant Network via Feature Disentanglement and Pose Restoration
PointCMC: Cross-Modal Multi-Scale Correspondences Learning for Point Cloud Understanding
MATE: Masked Autoencoders are Online 3D Test-Time Learners
Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding
Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding
Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting
On Automatic Data Augmentation for 3D Point Cloud Classification
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