Repositories
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nvdiffrast
Nvdiffrast - Modular Primitives for High-Performance Differentiable Rendering
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stylegan2-ada-pytorch
StyleGAN2-ADA - Official PyTorch implementation
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UnseenObjectClustering
Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation
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PoseCNN-PyTorch
PyTorch implementation of the PoseCNN framework
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NVAE
The Official PyTorch Implementation of "NVAE: A Deep Hierarchical Variational Autoencoder" (NeurIPS 2020 spotlight paper)
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stylegan2-ada
StyleGAN2 with adaptive discriminator augmentation (ADA) - Official TensorFlow implementation
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stylegan
StyleGAN - Official TensorFlow Implementation
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stylegan2
StyleGAN2 - Official TensorFlow Implementation
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cub
Forked from NVIDIA/cubTHIS REPOSITORY HAS MOVED TO github.com/nvidia/cub, WHICH IS AUTOMATICALLY MIRRORED HERE.
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torchmore
Forked from tmbdev/torchmore -
tarp
Forked from tmbdev/tarpFast and simple stream processing of files in tar files, useful for deep learning, big data, and many other applications.
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PAMTRI
PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification (ICCV 2019) - Official PyTorch Implementation
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deformable_object_grasping
This package provides a framework to automatically perform grasp tests on an arbitrary object model of choice.
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DeepIM-PyTorch
PyTorch implementation of the DeepIM framework
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imaginaire
NVIDIA PyTorch GAN library with distributed and mixed precision support
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UMR
Self-supervised Single-view 3D Reconstruction
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webdataset
Forked from tmbdev/webdataset -
few-shot-vid2vid
Pytorch implementation for few-shot photorealistic video-to-video translation.
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condensa
Programmable Neural Network Compression
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pacnet
Pixel-Adaptive Convolutional Neural Networks (CVPR '19)
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latentfusion
LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation
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cule
CuLE: A CUDA port of the Atari Learning Environment (ALE)
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matchlib
SystemC/C++ library of commonly-used hardware functions and components for HLS.
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acronym
This repository contains a sample of the grasping dataset and tools to visualize grasps, generate random scenes, and render observations. The two sample files are in the HDF5 format.