Papers 3D Shape Classification
“3D Shape Classification” 태그가 달린 논문 85편 · 필터 해제
Optimizing Multi-Modal Models for Image-Based Shape Retrieval: The Role of Pre-Alignment and Hard Contrastive Learning
Image-based shape retrieval (IBSR) aims to retrieve 3D models from a database given a query image, hence addressing a classical task in computer vision, computer graphics, and robotics. Recent approaches typically rely o…
3D Shape ClassificationContrastive LearningMetric LearningPoint CloudsBenchmark-Ready 3D Anatomical Shape Classification
Progress in anatomical 3D shape classification is limited by the complexity of mesh data and the lack of standardized benchmarks, highlighting the need for robust learning methods and reproducible evaluation. We introduc…
3D Shape ClassificationAttention Maps in 3D Shape Classification for Dental Stage Estimation with Class Node Graph Attention Networks
Deep learning offers a promising avenue for automating many recognition tasks in fields such as medicine and forensics. However, the black-box nature of these models hinders their adoption in high-stakes applications whe…
3D Shape ClassificationA comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation
Point cloud analysis has a wide range of applications in many areas such as computer vision, robotic manipulation, and autonomous driving. While deep learning has achieved remarkable success on image-based tasks, there a…
3D Point Cloud Classification3D Shape ClassificationAutonomous DrivingDeep Learning+2Interpretable3D: An Ad-Hoc Interpretable Classifier for 3D Point Clouds
3D decision-critical tasks urgently require research on explanations to ensure system reliability and transparency. Extensive explanatory research has been conducted on 2D images, but there is a lack in the 3D field. Fur…
3D Shape ClassificationEnsemble Quadratic Assignment Network for Graph Matching
Graph matching is a commonly used technique in computer vision and pattern recognition. Recent data-driven approaches have improved the graph matching accuracy remarkably, whereas some traditional algorithm-based methods…
3D Shape ClassificationGPUGraph MatchingGraph Neural NetworkInvariant Training 2D-3D Joint Hard Samples for Few-Shot Point Cloud Recognition
We tackle the data scarcity challenge in few-shot point cloud recognition of 3D objects by using a joint prediction from a conventional 3D model and a well-trained 2D model. Surprisingly, such an ensemble, though seems t…
3D Shape ClassificationRetrievalRotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds
Rotational invariance is a popular inductive bias used by many fields in machine learning, such as computer vision and machine learning for quantum chemistry. Rotation-invariant machine learning methods set the state of …
3D Shape ClassificationInductive BiasMolecular Property PredictionProperty PredictionMulti-View Representation is What You Need for Point-Cloud Pre-Training
A promising direction for pre-training 3D point clouds is to leverage the massive amount of data in 2D, whereas the domain gap between 2D and 3D creates a fundamental challenge. This paper proposes a novel approach to po…
3D geometry3D Object Detection3D Shape Classificationobject-detection+4APPT : Asymmetric Parallel Point Transformer for 3D Point Cloud Understanding
Transformer-based networks have achieved impressive performance in 3D point cloud understanding. However, most of them concentrate on aggregating local features, but neglect to directly model global dependencies, which r…
3D Part Segmentation3D Semantic Segmentation3D Shape ClassificationSegmentation+1ViPFormer: Efficient Vision-and-Pointcloud Transformer for Unsupervised Pointcloud Understanding
Recently, a growing number of work design unsupervised paradigms for point cloud processing to alleviate the limitation of expensive manual annotation and poor transferability of supervised methods. Among them, CrossPoin…
3D Shape ClassificationContrastive LearningSemantic SegmentationFrequency-domain Learning for Volumetric-based 3D Data Perception
Frequency-domain learning draws attention due to its superior tradeoff between inference accuracy and input data size. Frequency-domain learning in 2D computer vision tasks has shown that 2D convolutional neural networks…
3D Shape ClassificationSemantic SegmentationRobust 3D Shape Classification via Non-Local Graph Attention Network
We introduce a non-local graph attention network (NLGAT), which generates a novel global descriptor through two sub-networks for robust 3D shape classification. In the first sub-network, we capture the global relatio…
3D Shape ClassificationClassificationGraph AttentionRobust classificationRethinking Rotation Invariance with Point Cloud Registration
Recent investigations on rotation invariance for 3D point clouds have been devoted to devising rotation-invariant feature descriptors or learning canonical spaces where objects are semantically aligned. Examinations of l…
3D Shape ClassificationPoint Cloud RegistrationRetrievalMVTN: 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 RecognitionRetrievalLCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers
Transformer with its underlying attention mechanism and the ability to capture long-range dependencies makes it become a natural choice for unordered point cloud data. However, separated local regions from the general sa…
3D Object Detection3D Point Cloud Classification3D Semantic Segmentation3D Shape Classification+3PointMCD: 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 LearningVN-Transformer: Rotation-Equivariant Attention for Vector Neurons
Rotation equivariance is a desirable property in many practical applications such as motion forecasting and 3D perception, where it can offer benefits like sample efficiency, better generalization, and robustness to inpu…
3D Shape ClassificationMotion ForecastingMasked Discrimination for Self-Supervised Learning on Point Clouds
Masked autoencoding has achieved great success for self-supervised learning in the image and language domains. However, mask based pretraining has yet to show benefits for point cloud understanding, likely due to standar…
3D Shape ClassificationBinary ClassificationFew-Shot 3D Point Cloud Classificationobject-detection+2diffConv: Analyzing Irregular Point Clouds with an Irregular View
Standard spatial convolutions assume input data with a regular neighborhood structure. Existing methods typically generalize convolution to the irregular point cloud domain by fixing a regular "view" through e.g. a fixed…
3D Object Classification3D Point Cloud Classification3D Shape Classification