Neural networks that natively understand point clouds, meshes, and voxels. Learn the architectures, the training workflows, and the practical tricks that make 3D deep learning work on real datasets.
The complete guide to predictive digital twins built from survey data: asset cells, a condition index you can defend, a graph, a declared decay mechanism, a graph attention forecaster in plain PyTorch, and a work order that stops at a budget line.
Predictive Digital Twins and Spatiotemporal Simulation Guide Read More »
The complete 101 on 3D deep learning in Python: why point clouds break ordinary networks, permutation invariance, honest data prep, and the road from PointNet to KPConv.
3D Deep Learning Complete Guide Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 30 March 2025 This article compares the top 11 cloud tools that leverage 3D Generative AI. These 3D solutions simplify workflows and open new avenues for creative expression. These tools include Meshy, 3D AI Studio, Masterpiece […]
3D Generative AI: 11 Tools (Cloud) for 3D Model Generation Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 14 March 2025 This learning piece provides a step-by-step guide for developing your 3D Object Recognition application, from data collection to deployment. Learn about the different approaches to data collection and preparation, the importance of feature […]
Building a 3D Object Recognition Algorithm: A Step-by-Step Guide Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 4 February 2025 Monocular depth estimation turns a single photograph into a 3D model by predicting a depth value for every pixel, then lifting those pixels into a point cloud and meshing it. The pipeline here […]
3D Reconstruction from a Single Image: Tutorial Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 8 December 2024 We dive into SuperPoint Transformer, a novel approach for 3D semantic segmentation presented in the research paper “Efficient 3D Semantic Segmentation with Superpoint Transformer“. We also explore the core concepts, examine the research […]
Tutorial for 3D Semantic Segmentation with Superpoint Transformer Read More »
