3D Data Science: Learning Center

Articles, Guides and Resources that relates to 3D Data.
In detail, we focus on 3D Reconstruction, Point Cloud Processing and AI Sytems for 3D Data.

3D Master Toolbox

3D Reconstruction Methods, Hardware and Tools for 3D Data Processing

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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 provides 47 methods and AI tools for 3D. This article shares online solutions for 3D Data Curation (multi-modal 3D asset, various data […]

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3D Mesh from Point Cloud: Python with Marching Cubes Tutorial

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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 build a simple web app with a graphical user interface (GUI). This method bypasses the limitations of other reconstruction techniques like Poisson

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3D Point Cloud of a City

How to Quickly Visualize Massive Point Clouds with a No-Code Framework

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The average LiDAR scan contains 250+ million points. Visualizing and sharing this data efficiently is a significant challenge for many professionals. This tutorial provides a no-code solution to visualize and manage massive point clouds early in the workflow. Introduction to Massive Point Cloud Data Processing Visualizing massive point clouds used to be a real headache.

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Building a 3D Object Recognition Algorithm: A Step-by-Step Guide

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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 engineering, and the process of selecting and training a machine learning model. Discover best practices for evaluating and deploying your model’s performance

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Generative AI for 3D Modeling

3D Generative AI: 11 Tools (Cloud) for 3D Model Generation

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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 X, Alpha 3D, Sloid, 3DFY AI, Spline, Luma AI, Rococo Vision, Ponzu, and Deep Motion. Each tool offers unique features like text-to-3D,

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3D Shape Detection with RANSAC and Python (Sphere and Plane)

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This tutorial will walk you through the process of detecting spheres and planes in 3D point clouds using RANSAC and Python. This is because 3D shape detection is a crucial task in computer vision and robotics, enabling machines to understand and interact with their environment. Specifically, RANSAC is widely used for robust model fitting in

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superpoint transformers by Damien Robert

Tutorial for 3D Semantic Segmentation with Superpoint Transformer

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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 methodology, and unpack the key takeaways from the paper, with one of its author. 1. Introduction to SuperPoint Transformer SuperPoint Transformers proposes

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