Results 41 to 50 of about 5,826,707 (172)
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data [PDF]
IEEE International Conference on Computer Vision, 2019 Deep learning techniques for point cloud data have demonstrated great potentials in solving classical problems in 3D computer vision such as 3D object classification and segmentation. Several recent 3D object classification methods have reported state-of-
M. Uy +4 more
semanticscholar +1 more source
Relation-Shape Convolutional Neural Network for Point Cloud Analysis [PDF]
Computer Vision and Pattern Recognition, 2019 Point cloud analysis is very challenging, as the shape implied in irregular points is difficult to capture. In this paper, we propose RS-CNN, namely, Relation-Shape Convolutional Neural Network, which extends regular grid CNN to irregular configuration ...
Yongcheng Liu +3 more
semanticscholar +1 more source
Sequential Point Clouds: A Survey [PDF]
arXiv, 2022 Point cloud has drawn more and more research attention as well as real-world applications. However, many of these applications (e.g. autonomous driving and robotic manipulation) are actually based on sequential point clouds (i.e. four dimensions) because the information of the static point cloud data could provide is still limited.
arxiv
Deep Closest Point: Learning Representations for Point Cloud Registration [PDF]
IEEE International Conference on Computer Vision, 2019 Point cloud registration is a key problem for computer vision applied to robotics, medical imaging, and other applications. This problem involves finding a rigid transformation from one point cloud into another so that they align. Iterative Closest Point
Yue Wang, J. Solomon
semanticscholar +1 more source
APR: Online Distant Point Cloud Registration Through Aggregated Point Cloud Reconstruction [PDF]
arXiv, 2023 For many driving safety applications, it is of great importance to accurately register LiDAR point clouds generated on distant moving vehicles. However, such point clouds have extremely different point density and sensor perspective on the same object, making registration on such point clouds very hard.
arxiv
Learned Gridification for Efficient Point Cloud Processing [PDF]
arXiv, 2023 Neural operations that rely on neighborhood information are much more expensive when deployed on point clouds than on grid data due to the irregular distances between points in a point cloud. In a grid, on the other hand, we can compute the kernel only once and reuse it for all query positions.
arxiv
A novel tree-structured point cloud dataset for skeletonization algorithm evaluation [PDF]
arXiv, 2020 Curve skeleton extraction from unorganized point cloud is a fundamental task of computer vision and three-dimensional data preprocessing and visualization. A great amount of work has been done to extract skeleton from point cloud. but the lack of standard datasets of point cloud with ground truth skeleton makes it difficult to evaluate these algorithms.
arxiv
Perceptual Quality Assessment of Colored 3D Point Clouds [PDF]
arXiv, 2021 The real-world applications of 3D point clouds have been growing rapidly in recent years, but not much effective work has been dedicated to perceptual quality assessment of colored 3D point clouds. In this work, we first build a large 3D point cloud database for subjective and objective quality assessment of point clouds.
arxiv
SO-Net: Self-Organizing Network for Point Cloud Analysis [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 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).
Jiaxin Li, Ben M. Chen, Gim Hee Lee
semanticscholar +1 more source
From a few Accurate 2D Correspondences to 3D Point Clouds [PDF]
arXiv, 2022 Key points, correspondences, projection matrices, point clouds and dense clouds are the skeletons in image-based 3D reconstruction, of which point clouds have the important role in generating a realistic and natural model for a 3D reconstructed object. To achieve a good 3D reconstruction, the point clouds must be almost everywhere in the surface of the
arxiv