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3D Point Cloud Reconstruction
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Encoding and reconstruction of 3D point clouds.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Argus: Leveraging Multiview Images for Improved 3-D Scene Understanding With Large Language Models
Asymmetric Dual Self-Distillation for 3D Self-Supervised Representation Learning
DiffS-NOCS: 3D Point Cloud Reconstruction through Coloring Sketches to NOCS Maps Using Diffusion Models
Rethinking Gradient-based Adversarial Attacks on Point Cloud Classification
SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds
DG-MVP: 3D Domain Generalization via Multiple Views of Point Clouds for Classification
Introducing the Short-Time Fourier Kolmogorov Arnold Network: A Dynamic Graph CNN Approach for Tree Species Classification in 3D Point Clouds
Consistency Diffusion Models for Single-Image 3D Reconstruction with Priors
Point-LN: A Lightweight Framework for Efficient Point Cloud Classification Using Non-Parametric Positional Encoding
RW-Net: Enhancing Few-Shot Point Cloud Classification with a Wavelet Transform Projection-based Network
Rethinking Masked Representation Learning for 3D Point Cloud Understanding
Hyperbolic-constraint Point Cloud Reconstruction from Single RGB-D Images
Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation
Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification
Efficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Net
Test-Time Adaptation in Point Clouds: Leveraging Sampling Variation with Weight Averaging
Constrained Diffusion Implicit Models
Low-Density 3D Point Cloud Classification
PointNet with KAN versus PointNet with MLP for 3D Classification and Segmentation of Point Sets
Robust 3D Point Clouds Classification based on Declarative Defenders
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
MIRACLE 3D: Memory-efficient Integrated Robust Approach for Continual Learning on Point Clouds via Shape Model construction
3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning
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
SA-MLP: A Low-Power Multiplication-Free Deep Network for 3D Point Cloud Classification in Resource-Constrained Environments
Positional Prompt Tuning for Efficient 3D Representation Learning
Temporal Reversed Training for Spiking Neural Networks with Generalized Spatio-Temporal Representation
PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders
DC3DO: Diffusion Classifier for 3D Objects
RISurConv: Rotation Invariant Surface Attention-Augmented Convolutions for 3D Point Cloud Classification and Segmentation
GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understanding
Transferable 3D Adversarial Shape Completion using Diffusion Models
Eidos: Efficient, Imperceptible Adversarial 3D Point Clouds
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