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Created by Nadav Schor , Oren Katzir , Hao Zhang , Daniel Cohen-Or .
The main features of nschor/componet are: 3D Reconstruction, Generative Adversarial Networks.
Projects with overlapping indexed features include: hao-hust/g2lgan — Three-dimensional content creation has been a central research area in computer graphics for decades. The main… colmap/colmap — COLMAP is a 3D scene reconstruction suite and C++ geometry library that implements a full structure-from-motion… kozistr/awesome-gans — Awesome-GANs is a curated resource list and research repository focused on the development and evaluation of… microsoft/trellis.2 — TRELLIS.2 is a generative image-to-3D system that creates high-resolution 3D assets with physically based rendering… ashawkey/stable-dreamfusion — This project is a diffusion-based 3D generator and image-to-3D reconstruction system. It translates natural language… mrforexample/comfyui-3d-pack — ComfyUI-3D-Pack is a suite of custom nodes for ComfyUI that enables 3D asset generation and rendering within a…
Three-dimensional content creation has been a central research area in computer graphics for decades. The main challenge is to minimize manual intervention, while still allowing the creation of a variety of plausible 3D objects. In this work, we present a global-to-local generative model to…
COLMAP is a 3D scene reconstruction suite and C++ geometry library that implements a full structure-from-motion pipeline. It functions as a GPU-accelerated photogrammetry tool and multi-view stereo framework designed to produce dense 3D geometry and watertight meshes from collections of 2D images. The project distinguishes itself through hardware-accelerated feature extraction and a modular camera modeling system that supports perspective, fisheye, and equirectangular lens types. It employs vocabulary tree image retrieval to efficiently identify similar images in large datasets and provides P
Awesome-GANs is a curated resource list and research repository focused on the development and evaluation of generative adversarial networks. It serves as a structured index for academic literature and open-source implementations dedicated to the creation of synthetic data generators. The project provides a framework for training competing neural networks to produce outputs that mimic the statistical properties of original datasets. It emphasizes the use of configuration-driven pipelines to manage model hyperparameters and dataset paths, facilitating reproducible research workflows and standa
This project is a diffusion-based 3D generator and image-to-3D reconstruction system. It translates natural language descriptions or two-dimensional images into three-dimensional assets using neural radiance fields and diffusion models. The system utilizes score-distillation sampling and diffusion-based guidance to refine 3D shapes without requiring 3D training data. It includes specialized tools for transforming neural representations into exportable meshes with texture and material data, as well as a pipeline for iterative optimization of geometry and textures. The project covers a broad r