Papers Unsupervised 3D Point Cloud Linear Evaluation
“Unsupervised 3D Point Cloud Linear Evaluation” 태그가 달린 논문 8편 · 필터 해제
Spatio-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+4Self-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+4SO-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 EvaluationLearning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
We study the problem of 3D object generation. We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volume…
3D Object Recognition3D Point Cloud Linear ClassificationGenerative Adversarial NetworkObject+2