Papers 3D Point Cloud Linear Classification
“3D Point Cloud Linear Classification” 태그가 달린 논문 21편 · 필터 해제
AdaCrossNet: Adaptive Dynamic Loss Weighting for Cross-Modal Contrastive Point Cloud Learning
Manual annotation of large-scale point cloud datasets is laborious due to their irregular structure. While cross-modal contrastive learning methods such as CrossPoint and CrossNet have progressed in utilizing multimodal …
3D Part Segmentation3D Point Cloud Classification3D Point Cloud Linear ClassificationContrastive Learning+1Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud
Recent advancements in self-supervised learning in the point cloud domain have demonstrated significant potential. However, these methods often suffer from drawbacks, including lengthy pre-training time, the necessity of…
3D Part Segmentation3D Point Cloud Classification3D Point Cloud Linear ClassificationClassification+2ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages. ShapeLLM is built upon…
3D geometry3D Object Captioning3D Point Cloud Classification3D Point Cloud Linear Classification+13CrossMoCo: 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+3Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
Mainstream 3D representation learning approaches are built upon contrastive or generative modeling pretext tasks, where great improvements in performance on various downstream tasks have been achieved. However, we find t…
3D Point Cloud Classification3D Point Cloud Linear ClassificationDecoderFew-Shot 3D Point Cloud Classification+2Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders
Pre-training by numerous image data has become de-facto for robust 2D representations. In contrast, due to the expensive data acquisition and annotation, a paucity of large-scale 3D datasets severely hinders the learning…
3D Point Cloud Classification3D Point Cloud Linear ClassificationDecoderFew-Shot 3D Point Cloud ClassificationPoint-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
Masked Autoencoders (MAE) have shown great potentials in self-supervised pre-training for language and 2D image transformers. However, it still remains an open question on how to exploit masked autoencoding for learning …
3D Object Detection3D Point Cloud Classification3D Point Cloud Linear ClassificationDecoder+7CrossPoint: 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+1Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning
This paper advocates the use of implicit surface representation in autoencoder-based self-supervised 3D representation learning. The most popular and accessible 3D representation, i.e., point clouds, involves discrete sa…
3D geometry3D Point Cloud Classification3D Point Cloud Linear ClassificationDecoder+3Progressive Seed Generation Auto-encoder for Unsupervised Point Cloud Learning
With the development of 3D scanning technologies, 3D vision tasks have become a popular research area. Owing to the large amount of data acquired by sensors, unsupervised learning is essential for understanding and utili…
3D Point Cloud Linear ClassificationPoint cloud reconstructionPoint-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
We present Point-BERT, a new paradigm for learning Transformers to generalize the concept of BERT to 3D point cloud. Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers. S…
3D Point Cloud Classification3D Point Cloud Linear ClassificationFew-Shot 3D Point Cloud ClassificationFew-Shot Point Cloud Classification+2Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds
To date, various 3D scene understanding tasks still lack practical and generalizable pre-trained models, primarily due to the intricate nature of 3D scene understanding tasks and their immense variations introduced by ca…
3D Object Detection3D Point Cloud Classification3D Point Cloud Linear Classification3D Semantic Segmentation+8Unsupervised Point Cloud Pre-Training via Occlusion Completion
We describe a simple pre-training approach for point clouds. It works in three steps: 1. Mask all points occluded in a camera view; 2. Learn an encoder-decoder model to reconstruct the occluded points; 3. Use the encoder…
3D Point Cloud Linear ClassificationDecoderFew-Shot 3D Point Cloud ClassificationPoint Cloud Classification+4Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination
Although unsupervised feature learning has demonstrated its advantages to reducing the workload of data labeling and network design in many fields, existing unsupervised 3D learning methods still cannot offer a generic n…
3D Point Cloud Linear Classification3D Semantic SegmentationSelf-supervised Learning of Point Clouds via Orientation Estimation
Point clouds provide a compact and efficient representation of 3D shapes. While deep neural networks have achieved impressive results on point cloud learning tasks, they require massive amounts of manually labeled data, …
3D Point Cloud Linear ClassificationSelf-Supervised LearningUnsupervised 3D Point Cloud Linear EvaluationMulti-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction
Unsupervised feature learning for point clouds has been vital for large-scale point cloud understanding. Recent deep learning based methods depend on learning global geometry from self-reconstruction. However, these meth…
3D Point Cloud Linear ClassificationUnsupervised 3D Point Cloud Linear EvaluationSelf-Supervised Deep Learning on Point Clouds by Reconstructing Space
Point clouds provide a flexible and natural representation usable in countless applications such as robotics or self-driving cars. Recently, deep neural networks operating on raw point cloud data have shown promising res…
3D Point Cloud Linear ClassificationDeep LearningGeneral ClassificationPoint Cloud Pre-training+4View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions
In this paper we present a novel unsupervised representation learning approach for 3D shapes, which is an important research challenge as it avoids the manual effort required for collecting supervised data. Our method tr…
3D Point Cloud Linear ClassificationPredictionRepresentation LearningSO-Net: Self-Organizing Network for Point Cloud Analysis
This paper presents SO-Net, a permutation invariant architecture for deep learning with orderless point clouds. The SO-Net models the spatial distribution of point cloud by building a Self-Organizing Map (SOM). Based on …
3D Part Segmentation3D Point Cloud Classification3D Point Cloud Linear ClassificationPoint cloud reconstruction+2FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
Recent deep networks that directly handle points in a point set, e.g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and segmentation. In this work, a novel end…
3D Point Cloud Linear ClassificationDecoderGeneral ClassificationUnsupervised 3D Point Cloud Linear Evaluation