zorah

zorah's Stars

• TobyLobster/ImportLDraw

  A Blender plug-in for importing LDraw file format Lego models and parts.

  Language:Python27447

• JoshTheDerf/LDRParser

  A recursive parser written in Python for LDR (LDraw) files to combine them into one, easily converted and rendered file. Supports both Python Dictionaries and JSON.

  Language:Python103

• Horizon2333/imagenet-autoencoder

  AutoEncoder trained on ImageNet

  Language:Python9120

• lucmos/relreps

  Relative representations can be leveraged to enable solving tasks regarding "latent communication": from zero-shot model stitching to latent space comparison between diverse settings.

  Language:Jupyter Notebook466

• czq142857/IM-NET-pytorch

  PyTorch 1.2 implementation of IM-NET.

  Language:Python19727

• aaronwalsman/ltron

  Language:Python575

• STRCSussex-UbiCompSiegen/dl_har_public

  Official GitHub page of the workshop paper publication "Increasing Replicability, Comparability and Collaboration in HAR Through a Common Code Base" presented at the 2022 IEEE International Conference on Pervasive Computing and Communications Work (PerCom 22') in Progress Session (WiP).

  Language:Python72

• nileshkulkarni/volumetricPrimitivesPytorch

  Pytorch Implementation of https://github.com/shubhtuls/volumetricPrimitives

  Language:Python5115

• shubhtuls/volumetricPrimitives

  Code release for "Learning Shape Abstractions by Assembling Volumetric Primitives " (CVPR 2017)

  Language:Lua15234

• czq142857/BSP-NET-pytorch

  PyTorch 1.2 implementation of BSP-NET.

  Language:Python17739

• pvnieo/DPFM

  Pytorch code for "DPFM: Deep Partial Functional Maps" - 3DV 2021 (Oral)

  Language:Python414

• cihanongun/Point-Cloud-Autoencoder

  Autoencoder model for Point Clouds implemented in PyTorch

  Language:Jupyter Notebook241

• antao97/UnsupervisedPointCloudReconstruction

  Experiments on unsupervised point cloud reconstruction.

  Language:Python16527

• davidstutz/pointnet-auto-encoder

  Torch PointNet auto encoder implementation.

  Language:C++204

• maxjiang93/ShapeFlow

  Learnable Deformations Among 3D Shapes.

  Language:Jupyter Notebook634

• riccardomarin/Diff-FMAPs-PyTorch

  Language:Jupyter Notebook147

• riccardomarin/Diff-FMaps

  Language:Python103

• pvnieo/FMNet-pytorch

  A Pytorch implementation of: "Deep Functional Maps: Structured Prediction for Dense Shape Correspondence"

  Language:Python17

• ranahanocka/point2mesh

  Reconstruct Watertight Meshes from Point Clouds [SIGGRAPH 2020]

  Language:Python1.1k122

• llorz/SGA19_zoomOut

  code for the paper "ZoomOut: Spectral Upsampling for Efficient Shape Correspondence"

  Language:MATLAB439

• HiddenRamblings/TagMo

  Language:JavaScript3k384

• mkazhdan/PoissonRecon

  Poisson Surface Reconstruction

  Language:C++1.6k425

• marvin-eisenberger/smooth-shells

  Multi-Scale Shape Registration with Functional Maps

  Language:MATLAB215

• CalciferZh/SMPL

  NumPy, TensorFlow and PyTorch implementation of human body SMPL model and infant body SMIL model.

  Language:Python878139

• MarcoAttene/MeshFix-V2.1

  Converts a raw digitized polygon mesh to a clean mesh where all the occurrences of a specific set of "defects" are corrected. Holes, self-intersections, degenerate and non-manifold elements are all replaced with valid configurations.

  Language:C++35966

• stared/thinking-in-tensors-writing-in-pytorch

  Thinking in tensors, writing in PyTorch (a hands-on deep learning intro)

  Language:Jupyter Notebook32944

• NVlabs/stylegan

  StyleGAN - Official TensorFlow Implementation

  Language:Python14.1k3.2k

• alecjacobson/gptoolbox

  Matlab toolbox for Geometry Processing.

  Language:MATLAB629166

• google/mannequinchallenge

  Inference code and trained models for "Learning the Depths of Moving People by Watching Frozen People."

  Language:Python493104

• gpeyre/2015-SIGGRAPH-convolutional-ot

  J. Solomon, F. de Goes, G. Peyré, M. Cuturi, A. Butscher, A. Nguyen, T. Du, L. Guibas. Convolutional Wasserstein Distances: Efficient Optimal Transportation on Geometric Domains. ACM Transactions on Graphics (Proc. SIGGRAPH 2015), 34(4), pp. 66:1–66:11, 2015

  Language:Matlab10137