Abstract: Efficient point cloud visualization is indispensable for practical applications. In the context of point cloud visualization, 3-D rendering can be viewed as the kernel that transforms 3-D ...
Abstract: As radar can directly provide the velocity of the targets in autonomous driving and is known for the robustness against adverse weather conditions, it plays an important role in contrast to ...
Abstract: Weakly supervised point cloud semantic segmentation methods that require 1% or fewer labels with the aim of realizing almost the same performance as fully supervised approaches have recently ...
Abstract: Accurate and automatic detection of road surface element (such as road marking or manhole cover) information is the basis and key to many applications. To efficiently obtain the information ...
This repository accompanies Learn Java Fundamentals by Jeff Friesen (Apress, 2024). Download the files as a zip using the green button, or clone the repository to your machine using Git.
Abstract: Point cloud completion is to restore complete 3D scenes and objects from incomplete observations or limited sensor data. Existing fully-supervised methods rely on paired datasets of ...
Abstract: Most existing multimodal point-view fusion models for 3-D shape recognition typically improve recognition accuracy through complex feature fusion mechanisms. However, these mechanisms ...
Abstract: Deep learning techniques have been evolving at a faster pace offering a common framework for developing models for various applications using remote sensing data. Availability of high ...
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