thehardwarestore.ai

SuperSplat is a browser-based 3D Gaussian Splatting editor built with the PlayCanvas engine and PCUI framework. It provides a full-featured editing environment for loading, inspecting, selecting, transforming, color-grading, and exporting Gaussian splat data (PLY, SOG, SPZ, Splat formats). The application supports real-time GPU-accelerated data processing (histograms, bounding boxes, picking), camera animation with keyframe tracks and spline interpolation, video export via WebCodecs, and publishing to the PlayCanvas platform. Its architecture is event-driven with a centralized command queue for serializing async GPU work, an undo/redo system, and a modular tool system covering selection (rect, lasso, polygon, brush, flood, eyedropper, sphere, box) and transformation (move, rotate, scale, measure, orient). The UI is fully localized into 9 languages and includes a service worker for offline caching.

Reall3dViewer is a high-performance 3D Gaussian Splatting (3DGS) web viewer built on Three.js. It streams and renders large-scale point cloud scenes (up to 150M points) at interactive frame rates across desktop and mobile devices. The viewer supports multiple input formats including PLY, SPLAT, SPX (proprietary high-compression format), SPZ, SOG, and GLB with the KHR_gaussian_splatting extension. Its architecture is event-driven and modular, with a central event system (Events.ts), dedicated loaders for each format, web workers for parallel data fetching and sorting, and a texture management pipeline that handles LOD tile streaming, frustum culling, and adaptive quality levels. Beyond basic viewing, the repository provides a full suite of interactive features: measurement and annotation tools (distance, area, point labels, 3D tags, circle plans), a map viewer (Reall3dMapViewer) with GIS basemap integration, a first-person/third-person player mode with physics (rapier3d), weather effects (rain, snow), audio-driven text overlays, and a fly-through camera path system. The code is published as an npm package (@reall3d/reall3dviewer) and includes comprehensive demo pages with scene configuration JSON files. While primarily an application library, the project also exhibits data/research characteristics through its custom SPX format specifications, advanced compression techniques (WebP, gzip, xz, zstd), and spherical harmonics support up to degree 3. The source code demonstrates high engineering craftsmanship in areas like GLSL shader modularization, WebAssembly integration for format parsing, and efficient web worker communication patterns. However, there are some maintenance risks including a large monolithic event constant file (200+ auto-incremented integers), hardcoded demo URLs, and a build script that embeds worker code as base64, which complicates debugging. No automated tests are present.

This repository is a curated 'awesome list' for 3D Gaussian Splatting (3DGS) research papers, complemented by a static site generator and a YAML editor. The core functional code reads a YAML database of ~500 papers and generates a single-page HTML site with search, filter by year/tag, and paper selection for sharing. It includes a PyQt6 GUI for managing the YAML file, an arXiv integration module to fetch metadata, a PDF thumbnail generator, and a link checker for CI. The frontend uses vanilla CSS and JS with no framework. The codebase is well-organized for its purpose but lacks deep abstraction; the template engine is a thin wrapper around string.Template, and the editor is tightly coupled to the application. The main value is the curated paper list itself rather than the software engineering.

This repository is a curated list of research papers and resources on editing techniques for Neural Radiance Fields (NeRF) and 3D Gaussian Splatting. It contains hundreds of entries organized by year and venue, covering seminal works, surveys, and recent advances in 3D scene editing, stylization, inpainting, relighting, and more. The repository is maintained by Weize Li and collaborators from multiple institutions. It serves as a comprehensive index for researchers and practitioners, but contains no executable code, libraries, or data pipelines. It is purely a reference list.

This repository implements Mobile-GS, a real-time Gaussian splatting approach for novel view synthesis optimized for mobile devices. The system trains 3D Gaussian representations from multi-view images, applies compression techniques (vector quantization, Huffman coding, GPCC) to reduce model size, and renders novel views via differentiable EWA (Elliptical Weighted Average) splatting. It includes training pipelines (train.py, pretrain.py), rendering engine (gaussian_renderer/), scene management, custom CUDA kernels for efficient rasterization, and evaluation metrics. The architecture follows a modular design with separate components for training, rendering, scene handling, and compression, optimized for deployment on mobile platforms with limited computational resources.