This is code to fit per-pixel environment map with spherical Gaussian lobes, using LBFGS optimization

Last update: Dec 14, 2022

Overview

Spherical Gaussian Optimization

This is code to fit per-pixel environment map with spherical Gaussian lobes, using LBFGS optimization. This code has been used in the following paper to generate ground-truth spherical Gaussian parameters.

Li, Z., Shafiei, M., Ramamoorthi, R., Sunkavalli, K., & Chandraker, M. (2020). Inverse rendering for complex indoor scenes: Shape, spatially-varying lighting and svbrdf from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2475-2484).

Please cite the paper if you find the code to be useful in your own project. Please refer to the papers for more implementation details.

Instructions

To run the code, use the command python optimEnvSplit.py --cuda --dataRoot DATA, where DATA is the path to the synthetic dataset.

Owner

GitHub Repository

automatic color-grading

color-matcher Description color-matcher enables color transfer across images which comes in handy for automatic color-grading of photographs, painting

168 Jan 05, 2023

Code and Data for NeurIPS2021 Paper "A Dataset for Answering Time-Sensitive Questions"

Time-Sensitive-QA The repo contains the dataset and code for NeurIPS2021 (dataset track) paper Time-Sensitive Question Answering dataset. The dataset

35 Nov 14, 2022

A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning", IJCAI-21

MERIT A PyTorch implementation of our IJCAI-21 paper Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning. Depen

32 Jan 02, 2023

SwinTrack: A Simple and Strong Baseline for Transformer Tracking

SwinTrack This is the official repo for SwinTrack. A Simple and Strong Baseline Prerequisites Environment conda (recommended) conda create -y -n SwinT

196 Jan 04, 2023

Official Pytorch implementation of ICLR 2018 paper Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge.

Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge: Official Pytorch implementation of ICLR 2018 paper Deep Learning for Phy

47 Nov 06, 2022

Generalized Proximal Policy Optimization with Sample Reuse (GePPO)

Generalized Proximal Policy Optimization with Sample Reuse This repository is the official implementation of the reinforcement learning algorithm Gene

9 Nov 28, 2022

Tgbox-bench - Simple TGBOX upload speed benchmark

TGBOX Benchmark This script will benchmark upload speed to TGBOX storage. Build

1 Jan 09, 2022

Full Stack Deep Learning Labs

Full Stack Deep Learning Labs Welcome! Project developed during lab sessions of the Full Stack Deep Learning Bootcamp. We will build a handwriting rec

1.2k Dec 31, 2022

Python interface for the DIGIT tactile sensor

DIGIT-INTERFACE Python interface for the DIGIT tactile sensor. For updates and discussions please join the #DIGIT channel at the www.touch-sensing.org

35 Dec 22, 2022

codes for "Scheduled Sampling Based on Decoding Steps for Neural Machine Translation" (long paper of EMNLP-2022)

Scheduled Sampling Based on Decoding Steps for Neural Machine Translation (EMNLP-2021 main conference) Contents Overview Background Quick to Use Furth

13 Jul 25, 2022

Implementation of the paper NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting.

Non-AR Spatial-Temporal Transformer Introduction Implementation of the paper NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series For

66 Nov 28, 2022

codebase for "A Theory of the Inductive Bias and Generalization of Kernel Regression and Wide Neural Networks"

Eigenlearning This repo contains code for replicating the experiments of the paper A Theory of the Inductive Bias and Generalization of Kernel Regress

45 Dec 02, 2022

A minimal implementation of Gaussian process regression in PyTorch

pytorch-minimal-gaussian-process In search of truth, simplicity is needed. There exist heavy-weighted libraries, but as you know, we need to go bare b

38 Nov 25, 2022

Self-supervised learning algorithms provide a way to train Deep Neural Networks in an unsupervised way using contrastive losses

Self-supervised learning Self-supervised learning algorithms provide a way to train Deep Neural Networks in an unsupervised way using contrastive loss

2 Mar 26, 2022