Papers 3D Object Classification
“3D Object Classification” 태그가 달린 논문 97편 · 필터 해제
Point Cloud Self-supervised Learning via 3D to Multi-view Masked Autoencoder
In recent years, the field of 3D self-supervised learning has witnessed significant progress, resulting in the emergence of Multi-Modality Masked AutoEncoders (MAE) methods that leverage both 2D images and 3D point cloud…
3D Object Classification3D Object DetectionFew-Shot Learningobject-detection+2Fast Sparse 3D Convolution Network with VDB
We proposed a new Convolution Neural Network implementation optimized for sparse 3D data inference. This implementation uses NanoVDB as the data structure to store the sparse tensor. It leaves a relatively small memory f…
3D Object ClassificationExtending Multi-modal Contrastive Representations
Multi-modal contrastive representation (MCR) of more than three modalities is critical in multi-modal learning. Although recent methods showcase impressive achievements, the high dependence on large-scale, high-quality p…
3D Object ClassificationRepresentation LearningText RetrievalUni3D: Exploring Unified 3D Representation at Scale
Scaling up representations for images or text has been extensively investigated in the past few years and has led to revolutions in learning vision and language. However, scalable representation for 3D objects and scenes…
3D Object ClassificationRetrievalZero-shot 3D classificationZero-shot 3D Point Cloud Classification+4PointLLM: Empowering Large Language Models to Understand Point Clouds
The unprecedented advancements in Large Language Models (LLMs) have shown a profound impact on natural language processing but are yet to fully embrace the realm of 3D understanding. This paper introduces PointLLM, a pre…
3D Object Captioning3D Object Classification3D Question Answering (3D-QA)Common Sense Reasoning+2An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease
Machine learning methods have shown large potential for the automatic early diagnosis of Alzheimer's Disease (AD). However, some machine learning methods based on imaging data have poor interpretability because it is usu…
3D Object ClassificationInterpretable Machine LearningCrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud
The point cloud is a 3D geometric data that lacks a specific structure and is permutation-invariant. The applications of point clouds have gained significant attention recently in the field of vision tasks. However, most…
3D Object Classification3D Point Cloud Classification3D Point Cloud Linear ClassificationContrastive Learning+3Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation
Discovering inter-point connection for efficient high-dimensional feature extraction from point coordinate is a key challenge in processing point cloud. Most existing methods focus on designing efficient local feature ex…
3D Object Classification3D Part SegmentationInductive BiasPoint Cloud Classification+1MVImgNet: A Large-scale Dataset of Multi-view Images
Being data-driven is one of the most iconic properties of deep learning algorithms. The birth of ImageNet drives a remarkable trend of "learning from large-scale data" in computer vision. Pretraining on ImageNet to obtai…
3D Object ClassificationAttributeDensely Connected $G$-invariant Deep Neural Networks with Signed Permutation Representations
We introduce and investigate, for finite groups $G$, $G$-invariant deep neural network ($G$-DNN) architectures with ReLU activation that are densely connected-- i.e., include all possible skip connections. In contrast to…
3D Object ClassificationUnsupervised 3D Object Learning through Neuron Activity aware Plasticity
We present an unsupervised deep learning model for 3D object classification. Conventional Hebbian learning, a well-known unsupervised model, suffers from loss of local features leading to reduced performance for tasks wi…
3D Object ClassificationObjectPaRot: Patch-Wise Rotation-Invariant Network via Feature Disentanglement and Pose Restoration
Recent interest in point cloud analysis has led rapid progress in designing deep learning methods for 3D models. However, state-of-the-art models are not robust to rotations, which remains an unknown prior to real applic…
3D Object ClassificationDisentanglementHGNet: Learning Hierarchical Geometry From Points, Edges, and Surfaces
Parsing an unstructured point set into constituent local geometry structures (e.g., edges or surfaces) would be helpful for understanding and representing point clouds. This motivates us to design a deep architecture…
3D Object ClassificationAllSemantic SegmentationImproved Training for 3D Point Cloud Classification
The point cloud is a 3D geometric data of irregular format. As a result, they are needed to be transformed into 3D voxels or a collection of images before being fed into models. This unnecessarily increases the volume of…
3D Object Classification3D Point Cloud ClassificationClassificationPoint Cloud Classification+1PointCMC: Cross-Modal Multi-Scale Correspondences Learning for Point Cloud Understanding
Some self-supervised cross-modal learning approaches have recently demonstrated the potential of image signals for enhancing point cloud representation. However, it remains a question on how to directly model cross-modal…
3D Object ClassificationRepresentation LearningMATE: Masked Autoencoders are Online 3D Test-Time Learners
Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data. Like existing TTT methods…
3D Object ClassificationPoint Cloud ClassificationData Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding
Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods. However, data augmentation is not ideal as it requires a careful selection of th…
3D Object ClassificationContrastive LearningData AugmentationPseudo Label+1Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives
Numerous advancements in deep learning can be attributed to the access to large-scale and well-annotated datasets. However, such a dataset is prohibitively expensive in 3D computer vision due to the substantial collectio…
3D Object ClassificationDataset DistillationMulti-Task LearningObjectCrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding
Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular structure of point clouds. Self-supervised lea…
3D Object Classification3D Point Cloud Linear ClassificationContrastive LearningFew-Shot 3D Point Cloud Classification+1Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting
Learning from unlabeled or partially labeled data to alleviate human labeling remains a challenging research topic in 3D modeling. Along this line, unsupervised representation learning is a promising direction to auto-ex…
3D Object ClassificationClusteringRepresentation LearningSemantic Segmentation