"SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image", Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Humphrey Shi, Zhangyang Wang

Related tags

Deep Learningpytorchgannerfpytorch-lightningneural-radiance-fields
Overview

SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image

[Paper] [Website]

Pipeline

Code

Environment

pip install -r requirements.txt

Dataset Preparation

Please download the datasets from these links:

Please download the depth from here: https://drive.google.com/drive/folders/13Lc79Ox0k9Ih2o0Y9e_g_ky41Nx40eJw?usp=sharing

Training

If you meet OOM issue, try:

  1. enable precision=16
  2. reduce the patch size --patch_size (or --patch_size_x, --patch_size_y) and enlarge the stride size --sH, --sW
NeRF synthetic

  • Step 1

    python train.py  --dataset_name blender_ray_patch_1image_rot3d  --root_dir  ../../dataset/nerf_synthetic/lego   --N_importance 64 --img_wh 400 400 --num_epochs 3000 --batch_size 1  --optimizer adam --lr 2e-4  --lr_scheduler steplr --decay_step 1000 2000 --decay_gamma 0.5  --exp_name lego_s6 --with_ref --patch_size 64 --sW 6 --sH 6 --proj_weight 1 --depth_smooth_weight 0  --dis_weight 0 --num_gpus 4 --load_depth --depth_type nerf --model sinnerf --depth_weight 8 --vit_weight 10 --scan 4

  • Step 2

    python train.py  --dataset_name blender_ray_patch_1image_rot3d  --root_dir  ../../dataset/nerf_synthetic/lego   --N_importance 64 --img_wh 400 400 --num_epochs 3000 --batch_size 1  --optimizer adam --lr 1e-4  --lr_scheduler steplr --decay_step 1000 2000 --decay_gamma 0.5  --exp_name lego_s6_4ft --with_ref --patch_size 64 --sW 4 --sH 4 --proj_weight 1 --depth_smooth_weight 0.1  --dis_weight 0.1 --num_gpus 4 --load_depth --depth_type nerf --model sinnerf --depth_weight 8 --vit_weight 0 --pt_model xxx.ckpt --nerf_only  --scan 4
LLFF

  • Step 1

    python train.py  --dataset_name llff_ray_patch_1image_proj  --root_dir  ../../dataset/nerf_llff_data/room   --N_importance 64 --img_wh 504 378 --num_epochs 3000 --batch_size 1  --optimizer adam --lr 2e-4  --lr_scheduler steplr --decay_step 1000 2000 --decay_gamma 0.5  --exp_name llff_room_s4 --with_ref --patch_size_x 63 --patch_size_y 84 --sW 4 --sH 4 --proj_weight 1 --depth_smooth_weight 0  --dis_weight 0 --num_gpus 4 --load_depth --depth_type nerf --model sinnerf --depth_weight 8 --vit_weight 10

  • Step 2

    python train.py  --dataset_name llff_ray_patch_1image_proj  --root_dir  ../../dataset/nerf_llff_data/room   --N_importance 64 --img_wh 504 378 --num_epochs 3000 --batch_size 1  --optimizer adam --lr 1e-4  --lr_scheduler steplr --decay_step 1000 2000 --decay_gamma 0.5  --exp_name llff_room_s4_2ft --with_ref --patch_size_x 63 --patch_size_y 84 --sW 2 --sH 2 --proj_weight 1 --depth_smooth_weight 0.1  --dis_weight 0.1 --num_gpus 4 --load_depth --depth_type nerf --model sinnerf --depth_weight 8 --vit_weight 0 --pt_model xxx.ckpt --nerf_only
DTU

  • Step 1

    python train.py  --dataset_name dtu_proj  --root_dir  ../../dataset/mvs_training/dtu   --N_importance 64 --img_wh 640 512 --num_epochs 3000 --batch_size 1  --optimizer adam --lr 2e-4  --lr_scheduler steplr --decay_step 1000 2000 --decay_gamma 0.5  --exp_name dtu_scan4_s8 --with_ref --patch_size_y 70 --patch_size_x 56 --sW 8 --sH 8 --proj_weight 1 --depth_smooth_weight 0  --dis_weight 0 --num_gpus 4 --load_depth --depth_type nerf --model sinnerf --depth_weight 8 --vit_weight 10 --scan 4

  • Step 2

    python train.py  --dataset_name dtu_proj  --root_dir  ../../dataset/mvs_training/dtu   --N_importance 64 --img_wh 640 512 --num_epochs 3000 --batch_size 1  --optimizer adam --lr 1e-4  --lr_scheduler steplr --decay_step 1000 2000 --decay_gamma 0.5  --exp_name dtu_scan4_s8_4ft --with_ref --patch_size_y 70 --patch_size_x 56 --sW 4 --sH 4 --proj_weight 1 --depth_smooth_weight 0.1  --dis_weight 0.1 --num_gpus 4 --load_depth --depth_type nerf --model sinnerf --depth_weight 8 --vit_weight 0 --pt_model xxx.ckpt --nerf_only  --scan 4

More finetuning with smaller strides benefits reconstruction quality.

Testing

python eval.py  --dataset_name llff  --root_dir /dataset/nerf_llff_data/room --N_importance 64 --img_wh 504 378 --model nerf --ckpt_path ckpts/room.ckpt --timestamp test

Acknowledgement

Codebase based on https://github.com/kwea123/nerf_pl . Thanks for sharing!

Citation

If you find this repo is helpful, please cite:


@InProceedings{Xu_2022_SinNeRF,
author = {Xu, Dejia and Jiang, Yifan and Wang, Peihao and Fan, Zhiwen and Shi, Humphrey and Wang, Zhangyang},
title = {SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image},
journal={arXiv preprint arXiv:2204.00928},
year={2022}
}
Owner
VITA
Visual Informatics Group @ University of Texas at Austin
VITA
GitHub Repository
A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.

Imbalanced Dataset Sampler Introduction In many machine learning applications, we often come across datasets where some types of data may be seen more

Ming 2k Jan 08, 2023
Garbage classification using structure data.

垃圾分类模型使用说明 1.包含以下数据文件 文件 描述 data/MaterialMapping.csv 物体以及其归类的信息 data/TestRecords 光谱原始测试数据 CSV 文件 data/TestRecordDesc.zip CSV 文件描述文件 data/Boundaries.cs

wenqi 1 Dec 10, 2021
Experimenting with computer vision techniques to generate annotated image datasets from gameplay recordings automatically.

Experimenting with computer vision techniques to generate annotated image datasets from gameplay recordings automatically. The collected data will then be used to train a deep neural network that can

Martin Valchev 3 Apr 24, 2022
Official implementation of "Open-set Label Noise Can Improve Robustness Against Inherent Label Noise" (NeurIPS 2021)

Open-set Label Noise Can Improve Robustness Against Inherent Label Noise NeurIPS 2021: This repository is the official implementation of ODNL. Require

Hongxin Wei 12 Dec 07, 2022
Automatic learning-rate scheduler

AutoLRS This is the PyTorch code implementation for the paper AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly published

Yuchen Jin 33 Nov 18, 2022
Speed-Test - You can check your intenet speed using this tool

Speed-Test Tool By Hez_X AVAILABLE ON : Termux & Kali linux & Ubuntu (Linux E

Hez-X 3 Feb 17, 2022
The Self-Supervised Learner can be used to train a classifier with fewer labeled examples needed using self-supervised learning.

Published by SpaceML • About SpaceML • Quick Colab Example Self-Supervised Learner The Self-Supervised Learner can be used to train a classifier with

SpaceML 92 Nov 30, 2022
Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes

Neural Scene Flow Fields PyTorch implementation of paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes", CVPR 2021 [Projec

Zhengqi Li 583 Dec 30, 2022
Source code for Fixed-Point GAN for Cloud Detection

FCD: Fixed-Point GAN for Cloud Detection PyTorch source code of Nyborg & Assent (2020). Abstract The detection of clouds in satellite images is an ess

Joachim Nyborg 8 Dec 22, 2022
An Implementation of Fully Convolutional Networks in Tensorflow.

Update An example on how to integrate this code into your own semantic segmentation pipeline can be found in my KittiSeg project repository. tensorflo

Marvin Teichmann 1.1k Dec 12, 2022
Machine Learning University: Accelerated Computer Vision Class

Machine Learning University: Accelerated Computer Vision Class This repository contains slides, notebooks, and datasets for the Machine Learning Unive

AWS Samples 1.3k Dec 28, 2022
VoxHRNet - Whole Brain Segmentation with Full Volume Neural Network

VoxHRNet This is the official implementation of the following paper: Whole Brain Segmentation with Full Volume Neural Network Yeshu Li, Jonathan Cui,

Microsoft 12 Nov 24, 2022
Implementation of GGB color space

GGB Color Space This package is implementation of GGB color space from Development of a Robust Algorithm for Detection of Nuclei and Classification of

Resha Dwika Hefni Al-Fahsi 2 Oct 06, 2021
Code for IntraQ, PyTorch implementation of our paper under review

IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization paper Requirements Python = 3.7.10 Pytorch == 1.7

1 Nov 19, 2021
Escaping the Gradient Vanishing: Periodic Alternatives of Softmax in Attention Mechanism

Period-alternatives-of-Softmax Experimental Demo for our paper 'Escaping the Gradient Vanishing: Periodic Alternatives of Softmax in Attention Mechani

slwang9353 0 Sep 06, 2021
RuDOLPH: One Hyper-Modal Transformer can be creative as DALL-E and smart as CLIP

[Paper] [Хабр] [Model Card] [Colab] [Kaggle] RuDOLPH 🦌 🎄 ☃️ One Hyper-Modal Transformer can be creative as DALL-E and smart as CLIP Russian Diffusio

AI Forever 232 Jan 04, 2023
The coda and data for "Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach" (ACL '21)

We propose a hierarchical core-fringe learning framework to measure fine-grained domain relevance of terms – the degree that a term is relevant to a broad (e.g., computer science) or narrow (e.g., de

Jie Huang 14 Oct 21, 2022
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

Molecular Sets (MOSES): A benchmarking platform for molecular generation models Deep generative models are rapidly becoming popular for the discovery

MOSES 656 Dec 29, 2022
Python scripts form performing stereo depth estimation using the CoEx model in ONNX.

ONNX-CoEx-Stereo-Depth-estimation Python scripts form performing stereo depth estimation using the CoEx model in ONNX. Stereo depth estimation on the

Ibai Gorordo 8 Dec 29, 2022
PushForKiCad - AISLER Push for KiCad EDA

AISLER Push for KiCad Push your layout to AISLER with just one click for instant

AISLER 31 Dec 29, 2022