Papers 3D Classification
“3D Classification” 태그가 달린 논문 91편 · 필터 해제
Projector Is All You Train
The typical training process of a multimodal large language model (MLLM) involves adapting both the language model backbone and the projector between the backbone and a modality-specific encoder. We ask whether fine-tuni…
Spatial Reasoning3D Classification3D Classification of Paramagnetic Rim Lesions in Multiple Sclerosis via Asymmetric QSM-FLAIR Modeling
Paramagnetic rim lesions (Rim$^+$) identified on susceptibility-sensitive MRI have recently emerged as a specific biomarker of chronic active inflammation in Multiple Sclerosis (MS) and are associated with long-term disa…
Multimodal Deep Learning3D ClassificationPointLLM-R: Enhancing 3D Point Cloud Reasoning via Chain-of-Thought
Understanding 3D point clouds through language remains a fundamental challenge in computer graphics and visual computing, due to the irregular structure of point cloud data and the lack of explicit reasoning in existing …
3D ClassificationPoint CloudsAGA3DNet: Anatomy-Guided Gaussian Priors with Multi-view xLSTM for 3D Brain MRI Subtype Classification
Accurate 3D brain MRI subtype classification benefits from both localized anatomical cues and long-range contextual reasoning. We present AGA3DNet, a report-grounded framework that incorporates brief anatomical phrases e…
3D ClassificationDeep Reprogramming Distillation for Medical Foundation Models
Medical foundation models pre-trained on large-scale datasets have shown powerful versatile performance. However, when adapting medical foundation models for specific medical scenarios, it remains the inevitable challeng…
parameter-efficient fine-tuningKnowledge Distillation3D ClassificationFDIF: Formula-Driven supervised Learning with Implicit Functions for 3D Medical Image Segmentation
Deep learning-based 3D medical image segmentation methods relies on large-scale labeled datasets, yet acquiring such data is difficult due to privacy constraints and the high cost of expert annotation. Formula-Driven Sup…
Medical Image SegmentationRepresentation Learning3D ClassificationPlaneCycle: Training-Free 2D-to-3D Lifting of Foundation Models Without Adapters
Large-scale 2D foundation models exhibit strong transferable representations, yet extending them to 3D volumetric data typically requires retraining, adapters, or architectural redesign. We introduce PlaneCycle, a traini…
3D ClassificationNPNet: A Non-Parametric Network with Adaptive Gaussian-Fourier Positional Encoding for 3D Classification and Segmentation
We present NPNet, a fully non-parametric approach for 3D point-cloud classification and part segmentation. NPNet contains no learned weights; instead, it builds point features using deterministic operators such as farthe…
3D ClassificationRevisiting 2D Foundation Models for Scalable 3D Medical Image Classification
3D medical image classification is essential for modern clinical workflows. Medical foundation models (FMs) have emerged as a promising approach for scaling to new tasks, yet current research suffers from three critical …
Medical Image Classification3D ClassificationTomoGraphView: 3D Medical Image Classification with Omnidirectional Slice Representations and Graph Neural Networks
The sharp rise in medical tomography examinations has created a demand for automated systems that can reliably extract informative features for downstream tasks such as tumor characterization. Although 3D volumes contain…
Medical Image Classification3D ClassificationEnhancing Rotation-Invariant 3D Learning with Global Pose Awareness and Attention Mechanisms
Recent advances in rotation-invariant (RI) learning for 3D point clouds typically replace raw coordinates with handcrafted RI features to ensure robustness under arbitrary rotations. However, these approaches often suffe…
3D ClassificationPoint CloudsDoes DINOv3 Set a New Medical Vision Standard? Benchmarking 2D and 3D Classification, Segmentation, and Registration
The advent of large-scale vision foundation models, pre-trained on diverse natural images, has marked a paradigm shift in computer vision. However, how the frontier vision foundation models' efficacies transfer to specia…
3D Reconstruction3D ClassificationMVIP -- A Dataset and Methods for Application Oriented Multi-View and Multi-Modal Industrial Part Recognition
We present MVIP, a novel dataset for multi-modal and multi-view application-oriented industrial part recognition. Here we are the first to combine a calibrated RGBD multi-view dataset with additional object context such …
3D ClassificationSynthetic Data GenerationText-guided Synthetic Geometric Augmentation for Zero-shot 3D Understanding
Zero-shot recognition models require extensive training data for generalization. However, in zero-shot 3D classification, collecting 3D data and captions is costly and laborintensive, posing a significant barrier compare…
3D ClassificationZero-shot 3D classificationZero-Shot LearningSeeing is Not Believing: Adversarial Natural Object Optimization for Hard-Label 3D Scene Attacks
Deep learning models for 3D data have shown to be vulnerable to adversarial attacks, which have received increasing attention in various safety-critical applications such as autonomous driving and robotic navigation.…
3D ClassificationAutonomous DrivingMitigating Ambiguities in 3D Classification with Gaussian Splatting
3D classification with point cloud input is a fundamental problem in 3D vision. However, due to the discrete nature and the insufficient material description of point cloud representations, there are ambiguities in d…
3D ClassificationClassificationPeritumoral Expansion Radiomics for Improved Lung Cancer Classification
Purpose: This study investigated how nodule segmentation and surrounding peritumoral regions influence radionics-based lung cancer classification. Methods: Using 3D CT scans with bounding box annotated nodules, we genera…
3D ClassificationCancer ClassificationClassificationDiagnostic+2Beyond 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+1Hyperbolic Image-and-Pointcloud Contrastive Learning for 3D Classification
3D contrastive representation learning has exhibited remarkable efficacy across various downstream tasks. However, existing contrastive learning paradigms based on cosine similarity fail to deeply explore the potential i…
3D ClassificationClassificationContrastive LearningRepresentation Learning