Results 21 to 30 of about 5,826,707 (172)
Frequency-Selective Mesh-to-Mesh Resampling for Color Upsampling of Point Clouds [PDF]
2021 IEEE 23rd International Workshop on Multimedia Signal
Processing (MMSP), 2022 With the increased use of virtual and augmented reality applications, the importance of point cloud data rises. High-quality capturing of point clouds is still expensive and thus, the need for point cloud super-resolution or point cloud upsampling techniques emerges.
arxiv +1 more source
REGTR: End-to-end Point Cloud Correspondences with Transformers [PDF]
Computer Vision and Pattern Recognition, 2022 Despite recent success in incorporating learning into point cloud registration, many works focus on learning feature descriptors and continue to rely on nearest-neighbor feature matching and outlier filtering through RANSAC to obtain the final set of ...
Zi Jian Yew, Gim Hee Lee
semanticscholar +1 more source
Self-Positioning Point-Based Transformer for Point Cloud Understanding [PDF]
Computer Vision and Pattern Recognition, 2023 Transformers have shown superior performance on various computer vision tasks with their capabilities to capture long-range dependencies. Despite the success, it is challenging to directly apply Transformers on point clouds due to their quadratic cost in
Jinyoung Park +4 more
semanticscholar +1 more source
Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage: A Comprehensive Review
Remote Sensing, 2023 In the cultural heritage field, point clouds, as important raw data of geomatics, are not only three-dimensional (3D) spatial presentations of 3D objects but they also have the potential to gradually advance towards an intelligent data structure with ...
Su Yang, M. Hou, Songnian Li
semanticscholar +1 more source
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud [PDF]
Computer Vision and Pattern Recognition, 2020 In this paper, we propose a graph neural network to detect objects from a LiDAR point cloud. Towards this end, we encode the point cloud efficiently in a fixed radius near-neighbors graph. We design a graph neural network, named Point-GNN, to predict the
Weijing Shi, R. Rajkumar
semanticscholar +1 more source
Score-Based Point Cloud Denoising [PDF]
IEEE International Conference on Computer Vision, 2021 Point clouds acquired from scanning devices are often perturbed by noise, which affects downstream tasks such as surface reconstruction and analysis. The distribution of a noisy point cloud can be viewed as the distribution of a set of noise-free samples
Shitong Luo, Wei Hu
semanticscholar +1 more source
Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey [PDF]
, 2022 Point cloud data have been widely explored due to its superior accuracy and robustness under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved very impressive success in various applications such as surveillance and autonomous driving.
arxiv +1 more source
PF-Net: Point Fractal Network for 3D Point Cloud Completion [PDF]
Computer Vision and Pattern Recognition, 2020 In this paper, we propose a Point Fractal Network (PF-Net), a novel learning-based approach for precise and high-fidelity point cloud completion. Unlike existing point cloud completion networks, which generate the overall shape of the point cloud from ...
Zitian Huang +4 more
semanticscholar +1 more source
SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering [PDF]
arXiv, 2021 Point clouds obtained from 3D sensors are usually sparse. Existing methods mainly focus on upsampling sparse point clouds in a supervised manner by using dense ground truth point clouds. In this paper, we propose a self-supervised point cloud upsampling network (SSPU-Net) to generate dense point clouds without using ground truth.
arxiv
Progress and perspectives of point cloud intelligence
Geo-Spatial Information Science, 2023 With the rapid development of reality capture methods, such as laser scanning and oblique photogrammetry, point cloud data have become the third most important data source, after vector maps and imagery.
Bisheng Yang, Nobert Haala, Zhen Dong
semanticscholar +1 more source