3D Merry Christmas ?? - Point Cloud Resources 2020 Compilation

I compiled some interesting 2020 resources to dive into this holiday season, to prepare for the new year 2021. It deals mainly with Point Cloud Processing & Segmentation. You will find guides and tutorial to follow (Python, Julia), in-depth articles that target a specific research track and hands-on premium formations. With this content pack, you should know how to spend dead hours this holiday season! Have tons of fun ??!

3D Point Cloud Processing with Python

• Discover 3D Point Cloud Processing with Python: Tutorial to simply set up your python environment, start processing and visualize 3D point cloud data. Highlights Anaconda, NumPy, Matplotlib and Google Colab
• 5-Step Guide to generate 3D meshes from point clouds with Python: Tutorial to generate 3D meshes (.obj, .ply, .stl, .gltf) automatically from 3D point clouds using python. (Bonus) Surface reconstruction to create several Levels of Detail.
• How to automate LiDAR point cloud sub-sampling with Python: The ultimate guide to subsample 3D point clouds from scratch, with Python. Two efficient methods are shown to import, process, structure as a voxel grid, and visualise LiDAR data

Point Cloud visions & 3D clustering

• The Future of 3D Point Clouds: a new perspective. Discrete spatial datasets known as point clouds often lay the groundwork for decision-making applications. But can they become the next big thing?
• How to represent 3D Data? A visual guide to help choose data representations among 3D point clouds, meshes, parametric models, depth-maps, RGB-D, multi-view images, voxels…
• Fundamentals to clustering high-dimensional data (3D point clouds): Why unsupervised segmentation & clustering is the “bulk of AI”? What to look for when using them? How to evaluate performances? Explications and Illustration over 3D point cloud data.

Premium formations

• 3D Reconstructor: Hands-on formation providing you with focused knowledge immediately applied through the best open-source 3D Photogrammetry workflow. Unlock a clear action plan and new 3D passive income possibilities to integrate in your personal or professional career at no software costs.
• AR/VR Creator (pre-order): This formation provides a step-by-step workflow to process and set-up an environment captured using Photogrammetry for VR in Unity Engine. The entire execution focuses on a high-quality scene that is developed within a realistic production schedule. You will master key components from data capture, to 3D asset reworking and integration for VR app deployment
• 3D Segmentor (pre-order): Learn the fundamentals of Point Cloud Processing for 3D Object Detection, Segmentation and Classification. This online formation is for individuals and companies who rapidly want to increase their 3D Perception skills without spending hours browsing and figuring out how to do. Additionally, you can get direct access to working scripts and code to automate your processes

Top Research articles (2020)

• Poux, F. & al. (2020). A Built Heritage Information System Based on Point Cloud Data: HIS-PC. ISPRS International Journal of Geo-Information, 9(10), 588. https://hdl.handle.net/2268/251623
• Poux, F. & al. (2020). Initial User-Centered Design of a Virtual Reality Heritage System: Applications for Digital Tourism. Remote Sensing, 12(16), 2583. https://hdl.handle.net/2268/250248
• Bassier, M., Vergauwen, M., & Poux, F. (2020). Point Cloud vs. Mesh Features for Building Interior Classification. Remote Sensing, 12(14), 2224. https://hdl.handle.net/2268/249836
• Nys, G.-A., Poux, F., & Billen, R. (2020). CityJSON Building Generation from Airborne LiDAR 3D Point Clouds. ISPRS International Journal of Geo-Information, 9(521). https://hdl.handle.net/2268/250504
• Poux, F., Mattes, C., & Kobbelt, L. (2020). Unsupervised segmentation of indoor 3D point cloud: application to object-based classification. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIV-4(W1-2020), 111-118. https://hdl.handle.net/2268/250845
• Kharroubi, A., Billen, R., & Poux, F. (2020). MARKER-LESS MOBILE AUGMENTED REALITY APPLICATION FOR MASSIVE 3D POINT CLOUDS AND SEMANTICS. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII(B2), 255–261. https://hdl.handle.net/2268/250755
• Nys, G.-A., Billen, R., & Poux, F. (2020, August 12). Automatic 3D Buildings Compact Reconstruction from LiDAR point clouds. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (XLIII-B2-2020), 473-478. https://hdl.handle.net/2268/250754
• Poux, F., & Ponciano, J.-J. (2020). SELF-LEARNING ONTOLOGY FOR INSTANCE SEGMENTATION OF 3D INDOOR POINT CLOUD. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII(B2), 309–316. https://hdl.handle.net/2268/251067

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written by Florent Poux on the 23 December 2020.