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PU-Net: Point Cloud Upsampling Network, CVPR, 2018 (https://arxiv.org/abs/1801.06761)
The main features of yulequan/pu-net are: 3D Reconstruction, Computer Vision Research, Reconstruction and Completion, Registration and Reconstruction.
Projects with overlapping indexed features include: nywang16/pixel2mesh — This repository contains the TensorFlow implementation for the following paper. tonytheplaneswalker/pcn. liangliangnan/polyfit — Polygonal Surface Reconstruction from Point Clouds (C++ & Python). colmap/colmap — COLMAP is a 3D scene reconstruction suite and C++ geometry library that implements a full structure-from-motion… microsoft/trellis.2 — TRELLIS.2 is a generative image-to-3D system that creates high-resolution 3D assets with physically based rendering… mrforexample/comfyui-3d-pack — ComfyUI-3D-Pack is a suite of custom nodes for ComfyUI that enables 3D asset generation and rendering within a…
Polygonal Surface Reconstruction from Point Clouds (C++ & Python)
This repository contains the TensorFlow implementation for the following paper
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