A folder of phone photos already holds a 3D model. This complete guide walks 3D reconstruction in Python end to end, from parallax and photogrammetry through AI depth to Gaussian splatting, with the real cost of each spelled out plainly.
3D Reconstruction Complete Guide Read More »
A complete Python pipeline for 3D point cloud labeling from phone photos using interactive painting, pinhole projection, and KD-tree fusion — no GPU required.
3D Point Cloud Labeling from Photos with Python (and why AI can’t design it for you) Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 21 April 2025 The 3D Reconstruction pipeline, to get from 2D photographs to 3D models follows a structured path. This path consists of distinct steps that build upon each other to transform flat images into spatial […]
3D Reconstruction Pipeline: Photo to Model Workflow Guide Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 29 March 2025 3D Gaussian Splatting reconstructs a scene from a set of images or a video by representing it as a cloud of coloured Gaussians, each with a position, a covariance, a colour and an […]
3D Gaussian Splatting: Hands-on Course for Beginners Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 10 March 2025 3D sensors capture depth as well as colour, and they split into two families: active sensors that emit their own signal, such as LiDAR and structured light, and passive sensors that infer depth […]
3D Sensors Guide: Active vs. Passive Capture Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 28 February 2025 This tutorial dives deep into the Marching Cubes algorithm, a powerful technique for meshing 3D point clouds using Python. We transform a point cloud into a 3D mesh, experiment with various parameters, and […]
3D Mesh from Point Cloud: Python with Marching Cubes Tutorial 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 27 January 2025 This article provides the best recommendations and directions for every dataset, tool, software, hardware, code setup, and library for 3D Reconstruction Methods. This is the first Chapter of the 3D Master Toolbox, which […]
3D Reconstruction Methods, Hardware and Tools for 3D Data Processing Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 21 June 2024 This Live Course / Workshop series will give you a linear, hands-on solution for turning a monocular 2D Image to 3D Model. This means that you will be able to take any AI-generated […]
2D Image to 3D Model Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 5 January 2023 How-to Guide on 3D Reconstruction with Photogrammetry A full hands-on 3D Reconstruction tutorial using 3D Photogrammetry, Reality Capture, Meshroom, and Blender. What is 3D Reconstruction, in brief? The times we live in are super […]
How-to Guide on 3D Reconstruction with Photogrammetry Read More »
