SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer [PDF]
IEEE International Conference on Computer Vision, 2021 Point cloud completion aims to predict a complete shape in high accuracy from its partial observation. However, previous methods usually suffered from discrete nature of point cloud and unstructured prediction of points in local regions, which makes it ...
Peng Xiang +6 more
semanticscholar +1 more source
Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds
Remote Sensing, 2021 Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity.
Shiming Li +4 more
doaj +1 more source
A partial overlapping point cloud registration method based on dynamic feature matching
Dianxin kexue, 2021 The point cloud registration method can effectively complete the registration of point clouds with different overlap rates and various sizes, and ensure the accuracy of the 3D reconstruction model.To address the above issues, a partial overlapping point ...
Hui DU +3 more
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End-to-End Point Cloud Completion Network with Attention Mechanism
Sensors, 2022 We propose a conceptually simple, general framework and end-to-end approach to point cloud completion, entitled PCA-Net. This approach differs from the existing methods in that it does not require a “simple” network, such as multilayer perceptrons (MLPs),
Yaqin Li +4 more
doaj +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
Multistage Adaptive Point-Growth Network for Dense Point Cloud Completion
Remote Sensing, 2022 The point cloud data from actual measurements are often sparse and incomplete, making it difficult to apply them directly to visual processing and 3D reconstruction.
Ruidong Hao +6 more
doaj +1 more source
Projection-Based Point Convolution for Efficient Point Cloud Segmentation
IEEE Access, 2022 Understanding point cloud has recently gained huge interests following the development of 3D scanning devices and the accumulation of large-scale 3D data.
Pyunghwan Ahn +4 more
doaj +1 more source
Consistent ICP for the registration of sparse and inhomogeneous point clouds [PDF]
, 2016 In this paper, we derive a novel iterative closest point (ICP) technique that performs point cloud alignment in a robust and consistent way. Traditional ICP techniques minimize the point-to-point distances, which are successful when point clouds contain ...
Goeman, Werner +3 more
core +1 more source
The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions
, 2021 Recently, there has been a significant interest in performing convolution over irregularly sampled point clouds. Since point clouds are very different from regular raster images, it is imperative to study the generalization of the convolution networks more closely, especially their robustness under variations in scale and rotations of the input data ...
Li, Xingyi +3 more
openaire +2 more sources
NICP: Dense normal based point cloud registration [PDF]
, 2015 In this paper we present a novel on-line method to recursively align point clouds. By considering each point together with the local features of the surface (normal and curvature), our method takes advantage of the 3D structure around the points for the ...
Grisetti, Giorgio, Serafin, Jacopo
core +1 more source