Papers 3D Object Classification
“3D Object Classification” 태그가 달린 논문 97편 · 필터 해제
BrepLLM: Enabling Large Language Models to Understand Boundary Representations
Current token-sequence-based Large Language Models (LLMs) struggle to directly process 3D Boundary Representation (B-rep) models that contain complex geometric and topological information. To this end, we propose BrepLLM…
parameter-efficient fine-tuning3D Object ClassificationContrastive LearningBlendCLIP: Bridging Synthetic and Real Domains for Zero-Shot 3D Object Classification with Multimodal Pretraining
Zero-shot 3D object classification is crucial for real-world applications like autonomous driving, however it is often hindered by a significant domain gap between the synthetic data used for training and the sparse, noi…
3D Object ClassificationAutonomous DrivingDomain AdaptationTACO-Net: Topological Signatures Triumph in 3D Object Classification
3D object classification is a crucial problem due to its significant practical relevance in many fields, including computer vision, robotics, and autonomous driving. Although deep learning methods applied to point clouds…
3D Object ClassificationAutonomous DrivingPoint CloudsReinforced Embodied Active Defense: Exploiting Adaptive Interaction for Robust Visual Perception in Adversarial 3D Environments
Adversarial attacks in 3D environments have emerged as a critical threat to the reliability of visual perception systems, particularly in safety-sensitive applications such as identity verification and autonomous driving…
3D Object ClassificationAutonomous DrivingFace RecognitionRW-Net: Enhancing Few-Shot Point Cloud Classification with a Wavelet Transform Projection-based Network
In the domain of 3D object classification, a fundamental challenge lies in addressing the scarcity of labeled data, which limits the applicability of traditional data-intensive learning paradigms. This challenge is parti…
3D Object ClassificationFew-Shot LearningFew-Shot Point Cloud ClassificationPoint Cloud ClassificationPoint-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation
In recent years, the challenge of 3D shape analysis within point cloud data has gathered significant attention in computer vision. Addressing the complexities of effective 3D information representation and meaningful fea…
3D Object ClassificationClassificationScene SegmentationSegmentationEfficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Net
Recent advancements in machine learning, particularly through deep learning architectures like PointNet, have transformed the processing of three-dimensional (3D) point clouds, significantly improving 3D object classific…
3D Object ClassificationComputational EfficiencyBeyond local patches: Preserving global–local interactions by enhancing self-attention via 3D point cloud tokenization
Transformer-based architectures have recently shown impressive performance on various point cloud understanding tasks such as 3D object shape classification and semantic segmentation. Particularly, this can be attributed…
3D Classification3D Object Classification3D Part Segmentation3D Point Cloud Classification+3PointNet with KAN versus PointNet with MLP for 3D Classification and Segmentation of Point Sets
Kolmogorov-Arnold Networks (KANs) have recently gained attention as an alternative to traditional Multilayer Perceptrons (MLPs) in deep learning frameworks. KANs have been integrated into various deep learning architectu…
3D Classification3D Object Classification3D Point Cloud ClassificationKolmogorov-Arnold Networks+1MIRACLE 3D: Memory-efficient Integrated Robust Approach for Continual Learning on Point Clouds via Shape Model construction
In this paper, we introduce a novel framework for memory-efficient and privacy-preserving continual learning in 3D object classification. Unlike conventional memory-based approaches in continual learning that require sto…
3D Object ClassificationContinual LearningPrivacy PreservingFormula-Supervised Visual-Geometric Pre-training
Throughout the history of computer vision, while research has explored the integration of images (visual) and point clouds (geometric), many advancements in image and 3D object recognition have tended to process these mo…
3D Object Classification3D Object RecognitionObjectObject Recognition+1Continual Learning in 3D Point Clouds: Employing Spectral Techniques for Exemplar Selection
We introduce a novel framework for Continual Learning in 3D object classification (CL3D). Our approach is based on the selection of prototypes from each class using spectral clustering. For non-Euclidean data such as poi…
3D Object ClassificationClusteringContinual LearningGS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning
Self-supervised learning of point cloud aims to leverage unlabeled 3D data to learn meaningful representations without reliance on manual annotations. However, current approaches face challenges such as limited data dive…
3DGS3D Object ClassificationContrastive LearningData Augmentation+2PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders
Masked autoencoder has been widely explored in point cloud self-supervised learning, whereby the point cloud is generally divided into visible and masked parts. These methods typically include an encoder accepting visibl…
3D Object Classification3D Point Cloud ClassificationDecoderFew-Shot 3D Point Cloud Classification+4DC3DO: Diffusion Classifier for 3D Objects
Inspired by Geoffrey Hinton emphasis on generative modeling, To recognize shapes, first learn to generate them, we explore the use of 3D diffusion models for object classification. Leveraging the density estimates from t…
3D Object ClassificationClassificationMultimodal ReasoningObject+2Real-time object detection and tracking using flash LiDAR imagery
In this study, we present a real-time vehicle detection program that combines the You-Only-Look-Once-X (YOLOX) object detection algorithm with a multi-object Kalman filter tracker, specifically designed for analyzing 3D …
3D Object ClassificationObjectobject-detectionObject Detection+2MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors
Large 2D vision-language models (2D-LLMs) have gained significant attention by bridging Large Language Models (LLMs) with images using a simple projector. Inspired by their success, large 3D point cloud-language models (…
3D Object Captioning3D Object ClassificationGenerative 3D Object ClassificationGPU+1Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approach
Accurate classification of objects in 3D point clouds is a significant problem in several applications, such as autonomous navigation and augmented/virtual reality scenarios, which has become a research hot spot. In this…
3D Object ClassificationAutonomous NavigationPoint Cloud ClassificationOpen-Pose 3D Zero-Shot Learning: Benchmark and Challenges
With the explosive 3D data growth, the urgency of utilizing zero-shot learning to facilitate data labeling becomes evident. Recently, methods transferring language or language-image pre-training models like Contrastive L…
3D Object ClassificationClassificationTransfer Learningzero-shot-classification+1Improving Normalization with the James-Stein Estimator
Stein's paradox holds considerable sway in high-dimensional statistics, highlighting that the sample mean, traditionally considered the de facto estimator, might not be the most efficacious in higher dimensions. To addre…
3D Object Classificationimage-classificationImage ClassificationSemantic Segmentation