fkluger/cuboids_revisited

Robust Shape Fitting for 3D Scene Abstraction

Robust Shape Fitting for 3D Scene Abstraction

This repository contains the source code for the cuboid-based scene decomposition method described in our paper Robust Shape Fitting for 3D Scene Abstraction. It is extension of our previous work Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images. Please refer to the following repository if you are looking for the source code of the previous version: cuboids_revisited_cvpr21.

If you use this code, please cite both papers:

@article{kluger2024robust,
  title={Robust Shape Fitting for 3D Scene Abstraction},
  author={Kluger, Florian and Brachmann, Eric and Yang, Michael Ying and Rosenhahn, Bodo},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2024}
}

@inproceedings{kluger2021cuboids,
  title={Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images},
  author={Kluger, Florian and Ackermann, Hanno and Brachmann, Eric and Yang, Michael Ying and Rosenhahn, Bodo},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021}
}

For depth estimation, we utilise BTS. If you do as well, please also cite their paper:

@article{lee2019big,
  title={From big to small: Multi-scale local planar guidance for monocular depth estimation},
  author={Lee, Jin Han and Han, Myung-Kyu and Ko, Dong Wook and Suh, Il Hong},
  journal={arXiv preprint arXiv:1907.10326},
  year={2019}
}

Installation

Get the code:

git clone --recurse-submodules https://github.com/fkluger/cuboids_revisited.git
cd cuboids_revisited
git submodule update --init --recursive

Set up the Python environment using Anaconda:

conda env create -f environment.yml
source activate cuboids_pami
conda install pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -c conda-forge

Data

NYU Depth v2

In order to use the NYU Depth v2 dataset, you need to obtain the original MAT-file and convert it to a version 7 MAT-file in MATLAB so that we can load it via scipy:

load('nyu_depth_v2_labeled.mat')
save('nyu_depth_v2_labeled.v7.mat','-v7')

Then, extract all images and depth maps to separate Pickle files using our helper script:

python util/extract_nyu_to_files.py --source nyu_depth_v2_labeled.v7.mat --destination ./datasets/nyu_depth/files

Synthetic Metropolis Homographies (SMH)

Download the SMH dataset with depth maps from here: https://github.com/fkluger/smh

Pre-trained models

Download our pre-trained models, which we used for the experiments in our paper from here and place the files in the models directory.

If you want to train our method for RGB input, please also obtain the pre-trained weights for the BTS depth estimator from here and place them in the models folder as well.

Evaluation

In order to repeat the main experiments from our paper using pre-trained neural networks, you can run the following commands:

NYU

Ground truth depth, numerical solver

python evaluate.py --load models/nyu_gt_numerical/run1/consac_weights_best.net

This will run our method for depth input on the NYU test set with the parameters used in the paper and report all evaluation metrics at the end. Replace run1with run2 ... run5 to evaluate one of the other training runs.

Ground truth depth, neural solver

python evaluate.py --data_path datasets/nyu_depth/files --load models/nyu_gt_neural/run1/consac_weights_best.net --minsolver transformer --load_solver models/nyu_gt_neural/run1/primitive_fit_weights_best.net

RGB image, numerical solver

python evaluate.py --data_path datasets/nyu_depth/files --depth_model bts --load models/nyu_bts_numerical/run1/consac_weights_best.net --load_depth models/nyu_bts_numerical/run1/depth_weights_best.net 

RGB image, neural solver

python evaluate.py --data_path datasets/nyu_depth/files --depth_model bts --load models/nyu_bts_neural/run1/consac_weights_best.net --load_depth models/nyu_bts_neural/run1/depth_weights_best.net --minsolver transformer --load_solver models/nyu_bts_neural/run1/primitive_fit_weights_best.net

Synthetic Metropolis Homographies

Ground truth depth, numerical solver

python evaluate.py --dataset smh --data_path /path/to/smh -t 0.04 --a_min 2 --a_max 30 --load models/smh_gt_numerical/run1/consac_weights_best.net --instances 16 --fitting_lr 0.5

Ground truth depth, neural solver

python evaluate.py --dataset smh --data_path /path/to/smh -t 0.04 --a_min 2 --a_max 30 --load models/smh_gt_neural/run1/consac_weights_best.net --minsolver transformer --load_solver models/smh_gt_neural/run1/primitive_fit_weights_best.net

Additional options

Visualisation

Add the option --visualise to save plots visualising the results for each image. Set the destination folder with the --eval_results PATH option.

Training

Pre-Training: NYU

Sample weight network:

python train.py --train_consac --hyps 32 --data_path datasets/nyu_depth/files

Neural solver:

python pretrain_solver.py --dataset nyu 

Pre-Training: SMH

Sample weight network:

python train.py --dataset smh --data_path /path/to/smh --train_consac --hyps 32 --consac_lr 1e-6 --maximise_second_entropy 0.1 --epochs 100 --a_min 2.0 --a_min 30 --fitting_lr 0.5 

Neural solver:

python pretrain_solver.py --dataset smh

Fine-Tuning: NYU

Depth input, neural solver:

python train.py --minsolver transformer --train_consac --train_solver --consac_lr 1e-7 --solver_lr 1e-7 --softmax_alpha 1000 --max_prob_loss 0 --minimise_corr 0 --maximise_second_entropy 0 --hyps 32 --load models/nyu_gt_numerical/run1/consac_weights_best.net --load_solver models/solver/nyu/primitive_fit_weights_best.net --data_path datasets/nyu_depth/files

RGB input, numerical solver:

python train.py --depth_model bts --train_consac --train_depth --consac_lr 1e-7 --depth_lr 1e-7 --softmax_alpha 1000 --max_prob_loss 0 --minimise_corr 0 --maximise_second_entropy 0 --hyps 32 --load models/nyu_gt_numerical/run1/consac_weights_best.net

RGB input, neural solver:

python train.py --minsolver transformer --depth_model bts --train_consac --train_solver --train_depth --consac_lr 1e-7 --solver_lr 1e-7 --depth_lr 1e-7 --softmax_alpha 1000 --max_prob_loss 0 --minimise_corr 0 --maximise_second_entropy 0 --hyps 32 --load models/nyu_gt_numerical/run1/consac_weights_best.net --load_solver models/solver/nyu/primitive_fit_weights_best.net --data_path datasets/nyu_depth/files

Fine-Tuning: SMH

Depth input, neural solver:

python train.py --dataset smh --data_path /path/to/smh -t 0.04 --a_min 2 --a_max 30 --minsolver transformer --train_consac --train_solver --consac_lr 1e-8 --solver_lr 1e-8 --softmax_alpha 1000 --max_prob_loss 0 --minimise_corr 0 --maximise_second_entropy 0 --hyps 32 --load models/smh_gt_numerical/run1/consac_weights_best.net --load_solver models/solver/smh/primitive_fit_weights_best.net

You may want to use a separate GPU for --depth_gpu, as the whole pipeline does not fit on a single GPU with 12GB memory.