Papers 3D Point Cloud Classification
“3D Point Cloud Classification” 태그가 달린 논문 213편 · 필터 해제
Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification
Deploying 3D point cloud analysis in privacy-sensitive, resource-constrained settings faces two barriers: data cannot be centralized, and models must run on limited edge hardware. We present a multi-seed benchmark jointl…
3D Point Cloud ClassificationKnowledge DistillationFederated LearningTSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting
Although many complex models were proposed to analyze time series data, some studies have demonstrated remarkable performance with simpler structures. A recent study proposed a non-parametric framework for 3D point cloud…
3D Point Cloud ClassificationTime Series ForecastingLIDARLearn: A Unified Deep Learning Library for 3D Point Cloud Classification, Segmentation, and Self-Supervised Representation Learning
Three-dimensional (3D) point cloud analysis has become central to applications ranging from autonomous driving and robotics to forestry and ecological monitoring. Although numerous deep learning methods have been propose…
parameter-efficient fine-tuning3D Point Cloud ClassificationRepresentation LearningSemantic SegmentationLayered Quantum Architecture Search for 3D Point Cloud Classification
We introduce layered Quantum Architecture Search (layered-QAS), a strategy inspired by classical network morphism that designs Parametrised Quantum Circuit (PQC) architectures by progressively growing and adapting them. …
3D Point Cloud ClassificationHyQuRP: Hybrid quantum-classical neural network with rotational and permutational equivariance
Group-equivariant quantum machine learning has emerged as a promising paradigm by incorporating symmetry into quantum models. However, constructing models simultaneously equivariant to both rotational and permutational s…
3D Point Cloud ClassificationQuantum Machine LearningMapper-GIN: Lightweight Structural Graph Abstraction for Corrupted 3D Point Cloud Classification
Robust 3D point cloud classification is often pursued by scaling up backbones or relying on specialized data augmentation. We instead ask whether structural abstraction alone can improve robustness, and study a simple to…
3D Point Cloud ClassificationGraph ClassificationData AugmentationCertified L2-Norm Robustness of 3D Point Cloud Recognition in the Frequency Domain
3D point cloud classification is a fundamental task in safety-critical applications such as autonomous driving, robotics, and augmented reality. However, recent studies reveal that point cloud classifiers are vulnerable …
3D Point Cloud ClassificationAutonomous DrivingPoint CloudsPurge-Gate: Backpropagation-Free Test-Time Adaptation for Point Clouds Classification via Token Purging
Test-time adaptation (TTA) is crucial for mitigating performance degradation caused by distribution shifts in 3D point cloud classification. In this work, we introduce Token Purging (PG), a novel backpropagation-free app…
3D Point Cloud ClassificationTest-time AdaptationPoint CloudsEnhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet
The classification of 3D point clouds is crucial for applications such as autonomous driving, robotics, and augmented reality. However, the commonly used ModelNet40 dataset suffers from limitations such as inconsistent l…
3D Point Cloud ClassificationAutonomous DrivingPoint CloudsGenerating Adversarial Point Clouds Using Diffusion Model
Adversarial attack methods for 3D point cloud classification reveal the vulnerabilities of point cloud recognition models. This vulnerability could lead to safety risks in critical applications that use deep learning mod…
3D Point Cloud ClassificationAutonomous VehiclesAdversarial AttackPoint Clouds3D Test-time Adaptation via Graph Spectral Driven Point Shift
While test-time adaptation (TTA) methods effectively address domain shifts by dynamically adapting pre-trained models to target domain data during online inference, their application to 3D point clouds is hindered by the…
3D Point Cloud ClassificationTest-time AdaptationPoint CloudsAsymmetric Dual Self-Distillation for 3D Self-Supervised Representation Learning
Learning semantically meaningful representations from unstructured 3D point clouds remains a central challenge in computer vision, especially in the absence of large-scale labeled datasets. While masked point modeling (M…
3D Point Cloud ClassificationRepresentation LearningRethinking Gradient-based Adversarial Attacks on Point Cloud Classification
Gradient-based adversarial attacks have become a dominant approach for evaluating the robustness of point cloud classification models. However, existing methods often rely on uniform update rules that fail to consider th…
3D Point Cloud ClassificationPoint Cloud ClassificationSMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds
Test-Time Training (TTT) has emerged as a promising solution to address distribution shifts in 3D point cloud classification. However, existing methods often rely on computationally expensive backpropagation during adapt…
3D Point Cloud ClassificationComputational EfficiencyPoint Cloud ClassificationDG-MVP: 3D Domain Generalization via Multiple Views of Point Clouds for Classification
Deep neural networks have achieved significant success in 3D point cloud classification while relying on large-scale, annotated point cloud datasets, which are labor-intensive to build. Compared to capturing data with Li…
3D Point Cloud ClassificationDomain GeneralizationPoint Cloud ClassificationIntroducing the Short-Time Fourier Kolmogorov Arnold Network: A Dynamic Graph CNN Approach for Tree Species Classification in 3D Point Clouds
Accurate classification of tree species based on Terrestrial Laser Scanning (TLS) and Airborne Laser Scanning (ALS) is essential for biodiversity conservation. While advanced deep learning models for 3D point cloud class…
3D Point Cloud ClassificationPoint Cloud ClassificationPoint-LN: A Lightweight Framework for Efficient Point Cloud Classification Using Non-Parametric Positional Encoding
We introduce Point-LN, a novel lightweight framework engineered for efficient 3D point cloud classification. Point-LN integrates essential non-parametric components-such as Farthest Point Sampling (FPS), k-Nearest Neighb…
3D Point Cloud ClassificationClassificationPoint Cloud ClassificationAdaCrossNet: Adaptive Dynamic Loss Weighting for Cross-Modal Contrastive Point Cloud Learning
Manual annotation of large-scale point cloud datasets is laborious due to their irregular structure. While cross-modal contrastive learning methods such as CrossPoint and CrossNet have progressed in utilizing multimodal …
3D Part Segmentation3D Point Cloud Classification3D Point Cloud Linear ClassificationContrastive Learning+1Rethinking Masked Representation Learning for 3D Point Cloud Understanding
Self-supervised point cloud representation learning aims to acquire robust and general feature representations from unlabeled data. Recently, masked point modeling-based methods have shown significant performance improve…
3D Part Segmentation3D Point Cloud ClassificationFew-Shot 3D Point Cloud ClassificationRepresentation LearningPoint-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification
This paper introduces Point-GN, a novel non-parametric network for efficient and accurate 3D point cloud classification. Unlike conventional deep learning models that rely on a large number of trainable parameters, Point…
3D Point Cloud ClassificationClassificationPoint Cloud ClassificationTraining-free 3D Point Cloud Classification