Abstract
Most real-time terrain point cloud rendering techniques do not address the empty space between the points but rather try to minimize it by changing the way the points are rendered by either rendering them bigger or with more appropriate shapes such as paraboloids. In this work, we propose an alternative approach to point cloud rendering, which addresses the empty space between the points and tries to fill it with appropriate values to achieve the best possible output. The proposed approach runs in real time and outperforms several existing point cloud rendering techniques in terms of speed and render quality.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference42 articles.
1. A Low Cost 3D Scanner Based on Structured Light;Rocchini;Computer Graphics Forum,2001
2. Light Detection and Ranging (LIDAR): An Emerging Tool for Multiple Resource Inventory;Reutebuch;J. For.,2005
3. Lidar for self-driving cars;Hecht;Opt. Photonics News,2018
4. Rekleitis, I., Bedwani, J.L., and Dupuis, E. (2009, January 12–17). Autonomous Planetary Exploration Using LIDAR Data. Proceedings of the IEEE International Conference on Robotics and Automation, Kobe, Japan.
5. Complete Residential Urban Area Reconstruction from Dense Aerial LiDAR Point Clouds;Zhou;Graph. Model.,2013
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Intelligent Interface for Synthesizing Procedural Stone Forest Landscape;2023 2nd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR);2023-12-08
2. Synthesizing Realistic Cracked Terrain for Virtual Arid Environment Generation;2023 8th International Conference on Communication, Image and Signal Processing (CCISP);2023-11-17
3. Patch-based Monte Carlo Terrain Upsampling via Gaussian Laplacian Pyramids;Proceedings of the 2023 5th International Conference on Image, Video and Signal Processing;2023-03-24