Papers Point Cloud Classification
“Point Cloud Classification” 태그가 달린 논문 283편 · 필터 해제
Point-Selection Fine-Tuning Framework for Robust Point Cloud Classification
Noisy and corrupted points can substantially degrade point cloud recognition performance, especially under challenging corruption settings. In particular, full fine-tuning of 3D pre-trained models may amplify the influen…
Point Cloud ClassificationAn Enhanced Geometric-Spectral Feature Learning Framework for Airborne Multispectral Point Cloud Classification
Multispectral point cloud (MPC) is composed of 3D spatial-spectral information, which holds tremendous potential for accurate land-cover classification. However, the representation power of classification models is limit…
Point Cloud ClassificationA Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation
Point cloud stands as the most widely adopted format for representing 3D shapes and scenes due to its simplicity and geometric fidelity. However, its inherent unordered and irregular nature, exacerbated by sensor noise a…
Point Cloud ClassificationSemantic SegmentationA Unified Non-Parametric and Interpretable Point Cloud Analysis via t-FCW Graph Representation
We introduce an empowered transposed Fully Connected Weighted (t-FCW) graph representation to embed point clouds into a metric space. While original t-FCW has shown promising results for point cloud classification, the r…
Point Cloud ClassificationSemantic SegmentationPoint CloudsLearning Significant Persistent Homology Features for 3D Shape Understanding
Geometry and topology constitute complementary descriptors of three-dimensional shape, yet existing benchmark datasets primarily capture geometric information while neglecting topological structure. This work addresses t…
Point Cloud ClassificationCASL: Curvature-Augmented Self-supervised Learning for 3D Anomaly Detection
Deep learning-based 3D anomaly detection methods have demonstrated significant potential in industrial manufacturing. However, many approaches are specifically designed for anomaly detection tasks, which limits their gen…
Point Cloud ClassificationSelf-Supervised LearningRepresentation LearningAnomaly ClassificationHierarchical Direction Perception via Atomic Dot-Product Operators for Rotation-Invariant Point Clouds Learning
Point cloud processing has become a cornerstone technology in many 3D vision tasks. However, arbitrary rotations introduce variations in point cloud orientations, posing a long-standing challenge for effective representa…
Point Cloud ClassificationRepresentation LearningPoint CloudsSpatially Aware Linear Transformer (SAL-T) for Particle Jet Tagging
Transformers are very effective in capturing both global and local correlations within high-energy particle collisions, but they present deployment challenges in high-data-throughput environments, such as the CERN LHC. T…
Point Cloud ClassificationJet TaggingDAMM-LOAM: Degeneracy Aware Multi-Metric LiDAR Odometry and Mapping
LiDAR Simultaneous Localization and Mapping (SLAM) systems are essential for enabling precise navigation and environmental reconstruction across various applications. Although current point-to-plane ICP algorithms perfor…
Point Cloud ClassificationPose EstimationAdaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks
Canonicalization is a widely used strategy in equivariant machine learning, enforcing symmetry in neural networks by mapping each input to a standard form. Yet, it often introduces discontinuities that can affect stabili…
Point Cloud ClassificationData AugmentationPoint CloudsClebsch-Gordan Transformer: Fast and Global Equivariant Attention
The global attention mechanism is one of the keys to the success of transformer architecture, but it incurs quadratic computational costs in relation to the number of tokens. On the other hand, equivariant models, which …
Point Cloud ClassificationData AugmentationRobotic GraspingDesensitizing for Improving Corruption Robustness in Point Cloud Classification through Adversarial Training
Due to scene complexity, sensor inaccuracies, and processing imprecision, point cloud corruption is inevitable. Over-reliance on input features is the root cause of DNN vulnerabilities. It remains unclear whether this is…
Point Cloud ClassificationPoint CloudsJoint graph entropy knowledge distillation for point cloud classification and robustness against corruptions
Classification tasks in 3D point clouds often assume that class events \replaced{are }{follow }independent and identically distributed (IID), although this assumption destroys the correlation between classes. This \repla…
Point Cloud ClassificationKnowledge DistillationPoint CloudsDeep Lookup Network
Convolutional neural networks are constructed with massive operations with different types and are highly computationally intensive. Among these operations, multiplication operation is higher in computational complexity …
Point Cloud ClassificationImage Super-ResolutionImage ClassificationPoint CloudsPointDGRWKV: Generalizing RWKV-like Architecture to Unseen Domains for Point Cloud Classification
Domain Generalization (DG) has been recently explored to enhance the generalizability of Point Cloud Classification (PCC) models toward unseen domains. Prior works are based on convolutional networks, Transformer or Mamb…
Point Cloud ClassificationDomain GeneralizationPoint CloudsTowards a 3D Transfer-based Black-box Attack via Critical Feature Guidance
Deep neural networks for 3D point clouds have been demonstrated to be vulnerable to adversarial examples. Previous 3D adversarial attack methods often exploit certain information about the target models, such as model pa…
Point Cloud ClassificationAdversarial AttackPoint CloudsModelNet40-E: An Uncertainty-Aware Benchmark for Point Cloud Classification
We introduce ModelNet40-E, a new benchmark designed to assess the robustness and calibration of point cloud classification models under synthetic LiDAR-like noise. Unlike existing benchmarks, ModelNet40-E provides both n…
Point Cloud ClassificationPoint CloudsXAI for Point Cloud Data using Perturbations based on Meaningful Segmentation
We propose a novel segmentation-based explainable artificial intelligence (XAI) method for neural networks working on point cloud classification. As one building block of this method, we propose a novel point-shifting me…
Point Cloud ClassificationPoint Cloud SegmentationBeyondRPC: A Contrastive and Augmentation-Driven Framework for Robust Point Cloud Understanding
Robust perception of 3D point clouds remains a significant challenge in real-world environments where sensor data is often corrupted. While recent models and augmentation strategies have improved robustness individually,…
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 Classification