9 posts found
A beginner-friendly tour of Bayesian 3D reconstruction: why an ordinary 3D scan gives one confident answer everywhere, how turning geometry into a distribution produces honest confidence maps, and where the 'prior' that fills in unseen regions comes from.
3D Gaussian Splatting gives us a single best-fit scene — but no sense of where that geometry is trustworthy. This week we turn point-estimate splats into distributions over geometry, connecting Bayesian rendering, Fisher information, and diffusion priors back to last week's GP framework.
How motion estimation evolved from pixel-level optical flow to neural scene flow fields — and why it matters for reconstructing dynamic 3D worlds from video.
A step-by-step introduction to 4D Gaussian Splatting — how adding a time dimension to 3DGS enables real-time dynamic scene rendering, from deformation fields to temporal regularization.
How 4D Gaussian Splatting extends real-time radiance fields into the time domain — deformation fields, temporal regularization, neural scene flow, and the path to dynamic world models.
Understand the elegant trick that let transformers process images, why it surprisingly works, and why its fundamental limitations drive the next generation of vision architectures.
Map the modern image-to-3D ecosystem—from LRM-style direct regression and multi-view diffusion to latent 3D generation—and see how the frontier is now moving from static 3D to dynamic 4D worlds.
Understand 3D Gaussian Splatting from first principles—the real-time rendering technique powering next-generation world models like Marble.
Trend tutorial on World Models -- focusing on Worldlabs' Marble