A PyTorch Library for Accelerating 3D Deep Learning Research
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Updated
Jan 22, 2026 - Python
A PyTorch Library for Accelerating 3D Deep Learning Research
Quickly and accurately render even the largest data.
A geometry-shader-based, global CUDA sorted high-performance 3D Gaussian Splatting rasterizer. Can achieve a 5-10x speedup in rendering compared to the vanialla diff-gaussian-rasterization.
Python bindings to PDFium, reasonably cross-platform.
Original reference implementation of "StopThePop: Sorted Gaussian Splatting for View-Consistent Real-time Rendering"
Analysis of georeferenced rasters, vectors and point clouds
Enterprise-ready Document Analysis with Large Language Models
Differentiable Point Radiance Fields Rasteriser for Novel View Synthesis
A software 3d rasterizer for pygame (no other dependencies, e.g. numpy)
Gmesh supports differentiable rendering of mixed 3D Gaussians and meshes within a single scene.
Educational 3D Gaussian Splatting renderer in pure Python/PyTorch. Deliberately slow and explicit for learning. Official repo for Medium blog series.
HISDAC-ES: Creating historical settlement data for Spain (1900-2020) based on cadastral building footprint data
An improved AHN3 gridded DTM/DSM done as university project for the MSc Geomatics @ TU Delft
Some projects are modified from Chu-Song Chen's class of 3D Computer Vision with Deep Learning Applications at National Taiwan University.
Selected python scripts for geoprocessing using open source geospatial resources
Learning the basics of rendering with PyTorch3D, exploring 3D representations, and practicing constructing simple geometry.
A custom rasterization tool utilizing Pyngine to draw 3D objects.
OMTP performance collection
Implementação de métodos de rasterização - Computação Gráfica
Bridging raster & vector processing workflows without compromises - a lightweight, local, and non-destructive image-processing app
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