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.

Synthetic Point Cloud Generation of Rooms: Complete 3D Python Tutorial

This tutorial teaches you how to develop a Synthetic point cloud generation engine in Python using NumPy and Open3D, enabling the creation of unlimited labeled 3D data for machine learning. By automating the process, you can save time, reduce costs, and generate realistic training datasets without expensive equipment or manual labeling.

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Generative AI for 3D Modeling

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

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 Gaussian Splatting Live Course

3D Gaussian Splatting: Hands-on Course for Beginners

This Tutorial targets 3D Reconstruction and 3D Real-Time Visualization leveraging 3D Gaussian Splatting. In the end, you will be able to generate 3D Point Clouds and 3D Renderings from a Set of Images and/or Videos. 3D Reconstruction with 3D Gaussian Splatting In the rapidly evolving landscape of 3D modeling, innovations consistently push the envelope, enhancing

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

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

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 Reconstruction from a Single Image: Tutorial

This tutorial targets Monocular Depth Estimation for 3D Reconstruction (Point Cloud, 3D Mesh). In the end, you will be able to generate 3D Models from any photo or AI-generated image. 3D Reconstruction with 3D Deep Learning 3D Reconstruction Combining the power of deep learning unlocks the ability to get 3D models from a single image.

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3D Master Toolbox

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

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 Deep Learning Results of a Semantic Segmentation task on 3D Point Clouds

3D Deep Learning Essentials: Ressources, Roadmaps and Systems

Dive into the fascinating world of 3D with deep learning, where objects come alive and possibilities are beyond 2D pixels. Few technologies can change the landscape of Computer Science at the pace and impact that Artificial Intelligence does. But one specific aspect is even more profound: 3D Deep Learning. Why Learn 3D Deep Learning? The impact

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