Papers 3D Classification
“3D Classification” 태그가 달린 논문 91편 · 필터 해제
Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representation
In recent years, 3D understanding has turned to 2D vision-language pre-trained models to overcome data scarcity challenges. However, existing methods simply transfer 2D alignment strategies, aligning 3D representations w…
3D Classification3D Part Segmentation3D Point Cloud ClassificationRepresentation Learning+3Advancing Smart Malnutrition Monitoring: A Multi-Modal Learning Approach for Vital Health Parameter Estimation
Malnutrition poses a significant threat to global health, resulting from an inadequate intake of essential nutrients that adversely impacts vital organs and overall bodily functioning. Periodic examinations and mass scre…
3D ClassificationNutritionparameter estimationDiffCLIP: Leveraging Stable Diffusion for Language Grounded 3D Classification
Large pre-trained models have had a significant impact on computer vision by enabling multi-modal learning, where the CLIP model has achieved impressive results in image classification, object detection, and semantic seg…
3D ClassificationClassificationimage-classificationImage Classification+5OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding
We introduce OpenShape, a method for learning multi-modal joint representations of text, image, and point clouds. We adopt the commonly used multi-modal contrastive learning framework for representation alignment, but wi…
3D Classification3D Shape RepresentationContrastive LearningImage Generation+3ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding
Recent advancements in multimodal pre-training have shown promising efficacy in 3D representation learning by aligning multimodal features across 3D shapes, their 2D counterparts, and language descriptions. However, the …
3D Classification3D Point Cloud ClassificationRepresentation LearningText Generation+3Self-supervised Learning by View Synthesis
We present view-synthesis autoencoders (VSA) in this paper, which is a self-supervised learning framework designed for vision transformers. Different from traditional 2D pretraining methods, VSA can be pre-trained with m…
3D ClassificationDecoderSelf-Supervised LearningVariational Relational Point Completion Network for Robust 3D Classification
Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise, which hampers 3D geometric modeling and perception. Existing point cloud completion methods tend to generate global shape skeletons a…
3D ClassificationClassificationPoint Cloud CompletionAutomatic Aortic Valve Pathology Detection from 3-Chamber Cine MRI with Spatio-Temporal Attention Maps
The assessment of aortic valve pathology using magnetic resonance imaging (MRI) typically relies on blood velocity estimates acquired using phase contrast (PC) MRI. However, abnormalities in blood flow through the aortic…
3D ClassificationClassificationComparing 3D deformations between longitudinal daily CBCT acquisitions using CNN for head and neck radiotherapy toxicity prediction
Adaptive radiotherapy is a growing field of study in cancer treatment due to it's objective in sparing healthy tissue. The standard of care in several institutions includes longitudinal cone-beam computed tomography (CBC…
3D ClassificationMVTN: Learning Multi-View Transformations for 3D Understanding
Multi-view projection techniques have shown themselves to be highly effective in achieving top-performing results in the recognition of 3D shapes. These methods involve learning how to combine information from multiple v…
3D Classification3D Shape Classification3D Shape RecognitionRetrievalULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding
The recognition capabilities of current state-of-the-art 3D models are limited by datasets with a small number of annotated data and a pre-defined set of categories. In its 2D counterpart, recent advances have shown that…
3D Architecture3D Classification3D Point Cloud ClassificationClassification+5Local Neighborhood Features for 3D Classification
With advances in deep learning model training strategies, the training of Point cloud classification methods is significantly improving. For example, PointNeXt, which adopts prominent training techniques and InvResNet la…
3D ClassificationClassificationPoint Cloud ClassificationPointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
Large-scale pre-trained models have shown promising open-world performance for both vision and language tasks. However, their transferred capacity on 3D point clouds is still limited and only constrained to the classific…
3D Classification3D Object Detection3D Open-Vocabulary Instance Segmentation3D Part Segmentation+10PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial Attack
Despite recent success of self-supervised based contrastive learning model for 3D point clouds representation, the adversarial robustness of such pre-trained models raised concerns. Adversarial contrastive learning (ACL)…
3D ClassificationAdversarial AttackAdversarial RobustnessContrastive LearningSimpleView++: Neighborhood Views for Point Cloud Classification
Existing multi-view-based point cloud classification methods only utilize multiple views of point clouds and discard the point clouds from further processing. Among these methods, the Simple View model demonstrates that …
3D Classification3D Point Cloud ClassificationClassificationPoint Cloud ClassificationClassification of FIB/SEM-tomography images for highly porous multiphase materials using random forest classifiers
FIB/SEM tomography represents an indispensable tool for the characterization of three-dimensional nanostructures in battery research and many other fields. However, contrast and 3D classification/reconstruction problems …
3D ClassificationPointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
PointNet++ is one of the most influential neural architectures for point cloud understanding. Although the accuracy of PointNet++ has been largely surpassed by recent networks such as PointMLP and Point Transformer, we f…
3D Classification3D Part Segmentation3D Point Cloud Classification3D Semantic Segmentation+3APP-Net: Auxiliary-point-based Push and Pull Operations for Efficient Point Cloud Classification
Aggregating neighbor features is essential for point cloud classification. In the existing work, each point in the cloud may inevitably be selected as the neighbors of multiple aggregation centers, as all centers will ga…
3D Classification3D Point Cloud ClassificationGPUPoint Cloud ClassificationSplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems
We design deep neural networks (DNNs) and corresponding networks' splittings to distribute DNNs' workload to camera sensors and a centralized aggregator on head mounted devices to meet system performance targets in infer…
3D ClassificationDistributed ComputingNeural Architecture SearchM3T: Three-Dimensional Medical Image Classifier Using Multi-Plane and Multi-Slice Transformer
In this study, we propose a three-dimensional Medical image classifier using Multi-plane and Multi-slice Transformer (M3T) network to classify Alzheimer's disease (AD) in 3D MRI images. The proposed network synergica…
3D ClassificationClassificationInductive BiasRepresentation Learning