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Master the complete pipeline for reconstructing 3D models from images and sensor data: photogrammetry workflows from images to textured meshes, surface reconstruction from point clouds using Poisson and marching cubes, implicit surface representations with signed distance functions, mesh optimization and simplification, and texture mapping onto recovered geometry.
3D reconstruction is the process of recovering a complete digital 3D model of a scene or object from sensor data. This chapter covers the full pipeline: from raw images or point clouds through to watertight, textured meshes ready for visualization, simulation, or manufacturing.
What is this chapter about? We walk through the major reconstruction approaches. Photogrammetry takes a collection of photographs and produces a textured 3D model through a sequence of well-defined stages. Point cloud meshing converts raw 3D points into connected surfaces. Poisson reconstruction and marching cubes provide mathematically principled methods for extracting smooth surfaces from noisy data. Signed distance functions offer a powerful implicit representation that unifies many reconstruction techniques. We close with the practical concerns of mesh simplification (making models efficient) and texture mapping (making them look real).
Why does this matter? 3D reconstruction drives applications from cultural heritage preservation (digitizing ancient artifacts) to real estate (virtual property tours) to film and games (scanning real-world environments). Understanding the full pipeline lets you diagnose quality issues, choose appropriate methods for different scenarios, and build production-quality reconstruction systems.
How the topics connect: The photogrammetry pipeline provides the big picture of how images become 3D models. Mesh reconstruction and Poisson reconstruction convert intermediate point clouds into surfaces. Marching cubes extracts geometry from volumetric representations. Signed distance functions provide the mathematical foundation for implicit surfaces. Mesh simplification optimizes the result for practical use, and texture mapping adds visual realism.
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The full pipeline from photographs to 3D models — SfM, dense MVS, and the COLMAP workflow.
Two approaches to meshing — local connectivity and global smoothness
Converting raw point clouds into connected triangle meshes using ball pivoting and alpha shapes.
Extracting smooth, watertight surfaces by solving the Poisson equation on oriented normals.
Grid-based extraction and implicit representations
Extracting triangle meshes from volumetric scalar fields using cube classification and edge interpolation.
Implicit surface representations where zero-crossings define geometry — from TSDF to DeepSDF.
Optimization for rendering and adding visual realism
Reducing triangle count via edge collapse and quadric error metrics for efficient rendering.
Projecting color onto geometry through UV parameterization, atlas generation, and mipmap filtering.
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