Papers 3D Shape Recognition
“3D Shape Recognition” 태그가 달린 논문 35편 · 필터 해제
Shape and Texture Recognition in Large Vision-Language Models
Shape and texture recognition is fundamental to visual perception. The ability to identify shapes regardless of orientation, texture, or context, and to recognize textures independently of their associated objects, is es…
3D Shape Recognition3D Shape RetrievalMaterial RecognitionTexture Image RetrievalA Deep Learning Framework for Three Dimensional Shape Reconstruction from Phaseless Acoustic Scattering Far-field Data
The inverse scattering problem is of critical importance in a number of fields, including medical imaging, sonar, sensing, non-destructive evaluation, and several others. The problem of interest can vary from detecting t…
3D Shape RecognitionPEVA-Net: Prompt-Enhanced View Aggregation Network for Zero/Few-Shot Multi-View 3D Shape Recognition
Large vision-language models have impressively promote the performance of 2D visual recognition under zero/few-shot scenarios. In this paper, we focus on exploiting the large vision-language model, i.e., CLIP, to address…
3D Shape RecognitionFew-Shot LearningLanguage ModellingZero-Shot LearningGroup Multi-View Transformer for 3D Shape Analysis with Spatial Encoding
In recent years, the results of view-based 3D shape recognition methods have saturated, and models with excellent performance cannot be deployed on memory-limited devices due to their huge size of parameters. To address …
3D Classification3D Shape RecognitionKnowledge DistillationRetrievalMV-CLIP: Multi-View CLIP for Zero-shot 3D Shape Recognition
Large-scale pre-trained models have demonstrated impressive performance in vision and language tasks within open-world scenarios. Due to the lack of comparable pre-trained models for 3D shapes, recent methods utilize lan…
3D Classification3D Shape RecognitionZero-shot 3D classificationViewFormer: View Set Attention for Multi-view 3D Shape Understanding
This paper presents ViewFormer, a simple yet effective model for multi-view 3d shape recognition and retrieval. We systematically investigate the existing methods for aggregating multi-view information and propose a nove…
3D Shape Recognition3D Shape RetrievalRetrievalMVTN: 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 RecognitionRetrievalPointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape Recognition
As two fundamental representation modalities of 3D objects, 3D point clouds and multi-view 2D images record shape information from different domains of geometric structures and visual appearances. In the current deep lea…
3D Shape Classification3D Shape RecognitionTransfer LearningPoints to Patches: Enabling the Use of Self-Attention for 3D Shape Recognition
While the Transformer architecture has become ubiquitous in the machine learning field, its adaptation to 3D shape recognition is non-trivial. Due to its quadratic computational complexity, the self-attention operator qu…
3D Feature Matching3D Point Cloud Classification3D Shape RecognitionPoint Cloud RegistrationNeuralTailor: Reconstructing Sewing Pattern Structures from 3D Point Clouds of Garments
The fields of SocialVR, performance capture, and virtual try-on are often faced with a need to faithfully reproduce real garments in the virtual world. One critical task is the disentanglement of the intrinsic garment sh…
3D Shape Recognition3D Shape ReconstructionDiversityStructured Prediction+1PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation
3D shape representation and its processing have substantial effects on 3D shape recognition. The polygon mesh as a 3D shape representation has many advantages in computer graphics and geometry processing. However, there …
3D Object Classification3D Point Cloud Classification3D Shape Classification3D Shape Recognition+2Contrastive Learning of 3D Shape Descriptor with Dynamic Adversarial Views
View-based deep learning models have shown the capability to learn 3D shape descriptors with superior performance on 3D shape recognition, classification, and retrieval. Most popular techniques often leverage the class l…
3D Shape Classification3D Shape Recognition3D Shape RepresentationContrastive Learning+2LATFormer: Locality-Aware Point-View Fusion Transformer for 3D Shape Recognition
Recently, 3D shape understanding has achieved significant progress due to the advances of deep learning models on various data formats like images, voxels, and point clouds. Among them, point clouds and multi-view images…
3D Object Classification3D Object Retrieval3D Shape Recognition3D Shape Retrieval+1Learning Canonical View Representation for 3D Shape Recognition with Arbitrary Views
In this paper, we focus on recognizing 3D shapes from arbitrary views, i.e., arbitrary numbers and positions of viewpoints. It is a challenging and realistic setting for view-based 3D shape recognition. We propose a cano…
3D Shape Recognition3D Shape RepresentationAuto-MVCNN: Neural Architecture Search for Multi-view 3D Shape Recognition
In 3D shape recognition, multi-view based methods leverage human's perspective to analyze 3D shapes and have achieved significant outcomes. Most existing research works in deep learning adopt handcrafted networks as back…
3D Shape RecognitionNeural Architecture SearchRetrievalMVTN: Multi-View Transformation Network for 3D Shape Recognition
Multi-view projection methods have demonstrated their ability to reach state-of-the-art performance on 3D shape recognition. Those methods learn different ways to aggregate information from multiple views. However, the c…
3D Classification3D Object Retrieval3D Point Cloud Classification3D Shape Classification+4Learning Attentive and Hierarchical Representations for 3D Shape Recognition
This paper proposes a novel method for 3D shape representation learning, namely Hyperbolic Embedded Attentive Representation (HEAR). Different from existing multi-view based methods, HEAR develops a unified framework to …
3D Shape Classification3D Shape Recognition3D Shape Representation3D Shape Retrieval+2View-GCN: View-Based Graph Convolutional Network for 3D Shape Analysis
View-based approach that recognizes 3D shape through its projected 2D images has achieved state-of-the-art results for 3D shape recognition. The major challenge for view-based approach is how to aggregate multi-view feat…
3D Shape Classification3D Shape RecognitionRetrievalInvariant 3D Shape Recognition using Predictive Modular Neural Networks
In this paper PREMONN (PREdictive MOdular Neural Networks) model/architecture is generalized to functions of two variables and to non-Euclidean spaces. It is presented in the context of 3D invariant shape recognition and…
3D Shape RecognitionAction RecognitionDynamic Texture RecognitionGesture Recognition+2Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences
The success of supervised learning requires large-scale ground truth labels which are very expensive, time-consuming, or may need special skills to annotate. To address this issue, many self- or un-supervised methods are…
3D Part Segmentation3D Shape Classification3D Shape Recognition3D Shape Retrieval+2