3D Vision — One Shape, Five Ways to Store It | Stanford CS231N Lecture 15
CS231N Lecture 15 runs on one question: what data structure should a 3D shape use so a neural network can read it and produce it? The slides walk through five representations (depth map/surface normals, voxels, point clouds, triangle meshes, implicit surfaces), each with a signature architecture (fully convolutional depth prediction, 3D convolution, PointNet, Pixel2Mesh and Mesh R-CNN, DeepSDF). Then comes the speed trade-off between NeRF and 3D Gaussian Splatting, and a closing roll call of 2025–2026 models: VGGT, TRELLIS, Marble. The 2025 recording uses a different slide deck, with a different order and emphasis.