30 open-source projects similar to nmwsharp/learned-triangulation, ranked by how many features they have in common. Compare stars, activity and what each one does to find the best Learned Triangulation alternative.
Draco is a library and toolset for compressing, transcoding, and decoding 3D geometric meshes and point cloud data. Its primary purpose is to reduce storage size and transmission bandwidth for 3D assets. The project includes a geometry optimizer specifically for glTF file containers to reduce asset footprints. It also features a hardened decoder designed to process malformed or untrusted 3D geometric data safely to prevent memory corruption and crashes. The software covers a broad range of 3D data processing capabilities, including geometric data reconstruction, point attribute management, a
GET3D is a generative 3D mesh model and rendering framework designed to synthesize high-quality textured shapes and tetrahedral meshes. It functions as an image-to-3D reconstructor and text-to-3D generator, utilizing a differentiable 3D renderer to produce realistic visual perspectives and material effects. The system enables the creation of 3D assets from single 2D images, point clouds, or descriptive text prompts. It features a latent space interpolator for creating smooth transitions between different 3D objects and supports the independent control of geometry and texture. The project cov
Point-e is a system for 3D model synthesis that generates three-dimensional point clouds from natural language descriptions and two-dimensional images. It utilizes diffusion models to synthesize these spatial representations based on text prompts or source images. The project includes specialized tools for refining these outputs, such as a point cloud upsampler to increase the density and resolution of low-resolution models. It also provides a mesh converter that uses distance function regression to transform raw point cloud data into structured 3D meshes. The broader capability surface cove
Codes for Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance (ECCV2020). paper
Please see our follow-up work PPSurf. It's easier to use, much faster and better.
The reference implementaiton for the CVPR 2019 paper Deep Geometric Prior for Surface Reconstruction.
This repository contains the official implementation of the CVPR 2021 (Oral) paper Neural Splines: Fitting 3D Surfaces with Infinitely-Wide Neural Networks.
This repository contains code for the ICCV 2021 paper
Paper - Supplementaty - Project Website - Arxiv - Published in NeurIPS 2020.
This repository contains the code to reproduce the results from the paper.
Project Page | Personal Web Page | Paper
Personal Web Pages | Paper | Project Page This repository contains the code to reproduce the results from the paper. Surface Reconstruction from Point Clouds by Learning Predictive Context Priors.
This repository contains an implementation to the ICLR 2021 paper SALD: Sign Agnostic Learning with Derivatives.
This repository contains the implementation of our papers Dual Octree Graph Networks. The experiments are conducted on Ubuntu 18.04 with 4 V400 GPUs (32GB memory). The code is released under the MIT license.
A non-official PyTorch implementation for Scan2Mesh: From Unstructured Range Scans to 3D Meshes paper on ShapeNet dataset
This is our implementation of the paper Differentiable Surface Triangulation that enables optimization for any per-vertex or per-face differentiable objective function over the space of underlying surface triangulations.
This is our implementation of the paper "Learning Delaunay Surface Elements for Mesh Reconstruction" at CVPR 2021 (oral), a method for mesh recontruction from a point cloud.
Meshlet Priors for 3D Mesh Reconstruction Abhishek Badki, Orazio Gallo, Jan Kautz, Pradeep Sen IEEE CVPR 2020
Official implementation of Neural Vector Fields (NVF). Feel free to use this code for academic work, but please cite the following: `` @misc{yang2023neural, title={Neural Vector Fields: Implicit Representation by Explicit Learning}, author={Xianghui Yang and Guosheng Lin and Zhenghao Chen and…
If you find this project useful in your research, please consider citing:
` pytorch #1.10.0+cu111 pytorch3d #0.6.2 open3d trimesh point-cloud-utils cd pointnet2opslib python setup.py install Download the data from Google Drive (These shapes are processed by DISN, remove the interior and non-manifold structures.) Then use the codes in scripts to get the dataset.…
Preprint | Supplementary | Accepted at the International Conference on 3D Vision (3DV)
Paper | Supplementary | Slides | Video | Project Page