3D generation, computer vision, graphics, CUDA inference, and implementation details between commits.
Why generic compiler paths underperform on dynamic sparse 3D generative models—and how null-context attention removal and model-aware CUDA kernels cut inference latency.
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How a fast-follower team tracked the evolution of 3D AI to build an in-house generative 3D service—from VecSet VAEs to Voxel-conditioned 3D DiTs.
Why a Straight Line? Unpacking the Physics and Geometry of Rectified Flow
Comparing vecset and sparse-voxel VAE–DiT architectures, from 3D representation and compression to shape-generation design
Preparing training-ready 3D data through watertight mesh conversion, surface sampling, and salient-edge-aware preprocessing
Tracing 3D latent diffusion from ShapeVAE to TRELLIS and Hunyuan3D, and comparing how modern systems represent and generate shape
Applying NeRF and Gaussian Splatting beyond photography to X-ray tomography, plus a lightweight viewer workflow built with Viser.
How NVIDIA’s 3D Gaussian Ray Tracing replaces splat rasterization with differentiable ray tracing—and what that changes.
A ground-up guide to Gaussian Splatting and differentiable rasterization, tracing the mathematics through its CUDA implementation—from projection and covariance
Comparing VGGSfM and MASt3R as learned Structure-from-Motion pipelines for camera estimation and radiance-field reconstruction.
How 2D Gaussian Splatting replaces volumetric blobs with oriented disks to improve surface geometry and mesh reconstruction
Evaluating diffusion-estimated depth by turning Marigold predictions into textured meshes and inspecting their geometric consistency.
Testing whether video generation models encode enough 3D consistency to reconstruct radiance fields from their synthesized views
Examining the quality, rendering, and production constraints that limit NeRF in games—and the workarounds available today
Reviewing Instant-NGP’s multi-resolution hash encoding and rebuilding its NeRF pipeline step by step in PyTorch
Review of Fourier Features Let Networks Learn High-Frequency Functions
A rigorous derivation of why a signed distance field satisfies the Eikonal equation, from its geometric definition to gradient bounds
How Generalized Winding Numbers turn imperfect meshes into robust inside–outside tests for signed distance field construction
Comparing Google model-viewer with a custom Three.js pipeline for embedding, controlling, and presenting 3D assets on the web
A practical Three.js guide to embedding an interactive Gaussian Splatting scene in a website, with no web-graphics background required
Diagonalization, Matrix Decomposition and Principal Component Analysis (PCA)
Building intuition for linear systems and transformations through matrices, basis vectors, and geometric mappings
From camera projection to stereo correspondence, deriving the epipolar geometry used to recover 3D structure from two views.
How image transformations, homographies, feature correspondences, and alignment connect in practical computer vision pipelines.
A practical tour of image gradients, Sobel and LoG filters, and the feature detection principles built on top of them.
A practical introduction to spatial image filters and morphology, including erosion, dilation, opening, and closing.