Results 51 to 60 of about 5,826,707 (172)

D-Net: Learning for Distinctive Point Clouds by Self-Attentive Point Searching and Learnable Feature Fusion [PDF]

arXiv, 2023
Learning and selecting important points on a point cloud is crucial for point cloud understanding in various applications. Most of early methods selected the important points on 3D shapes by analyzing the intrinsic geometric properties of every single shape, which fails to capture the importance of points that distinguishes a shape from objects of ...
arxiv  

ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds [PDF]

arXiv, 2020
We introduce ShapeAdv, a novel framework to study shape-aware adversarial perturbations that reflect the underlying shape variations (e.g., geometric deformations and structural differences) in the 3D point cloud space. We develop shape-aware adversarial 3D point cloud attacks by leveraging the learned latent space of a point cloud auto-encoder where ...
arxiv  

Real-Time Spatio-Temporal LiDAR Point Cloud Compression [PDF]

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020
Compressing massive LiDAR point clouds in real-time is critical to autonomous machines such as drones and self-driving cars. While most of the recent prior work has focused on compressing individual point cloud frames, this paper proposes a novel system that effectively compresses a sequence of point clouds.
arxiv  

Multi-scale Receptive Fields Graph Attention Network for Point Cloud Classification [PDF]

arXiv, 2020
Understanding the implication of point cloud is still challenging to achieve the goal of classification or segmentation due to the irregular and sparse structure of point cloud. As we have known, PointNet architecture as a ground-breaking work for point cloud which can learn efficiently shape features directly on unordered 3D point cloud and have ...
arxiv  

FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017
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.
Yaoqing Yang, Chen Feng, Yiru Shen, Dong Tian   +3 more
semanticscholar   +1 more source

Multiple collisions in N59 bubble: Sequential cloud-cloud collisions [PDF]

arXiv
We report that the gas components in the N59 bubble suffered from sequential multiple cloud-cloud collision (CCC) processes. The molecular gas in the N59 bubble can be decomposed into four velocity components, namely Cloud A [95, 108] km s$^{-1}$, Cloud B [86, 95] km s$^{-1}$, Cloud C [79, 86] km s$^{-1}$ and Cloud D [65, 79] km s$^{-1}$.
arxiv  

GaussianPU: A Hybrid 2D-3D Upsampling Framework for Enhancing Color Point Clouds via 3D Gaussian Splatting [PDF]

arXiv
Dense colored point clouds enhance visual perception and are of significant value in various robotic applications. However, existing learning-based point cloud upsampling methods are constrained by computational resources and batch processing strategies, which often require subdividing point clouds into smaller patches, leading to distortions that ...
arxiv  

Reexamination of cloud point isotherms for the system polystyrene/polybutadiene/tetralin [PDF]

, 1978
Douglas R. Lloyd, Charles M. Burns
openalex   +1 more source

Efficient Point Clouds Upsampling via Flow Matching [PDF]

arXiv
Diffusion models are a powerful framework for tackling ill-posed problems, with recent advancements extending their use to point cloud upsampling. Despite their potential, existing diffusion models struggle with inefficiencies as they map Gaussian noise to real point clouds, overlooking the geometric information inherent in sparse point clouds.
arxiv  

Measurements of the Movement, Concentration and Dimensions of Clouds Resulting from Instantaneous Point Sources [PDF]

, 1971
P.W. Nickola
openalex   +1 more source