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ICRA 2018 "Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image" (PyTorch Implementation)
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| 2026-07-24 | 452 |
| 2026-07-25 | 452 |
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| 2026-08-06 | 452 |
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sparse-to-dense.pytorch ============================ This repo implements the training and testing of deep regression neural networks for ["Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image"](https://arxiv.org/pdf/1709.07492.pdf) by [Fangchang Ma](http://www.mit.edu/~fcma) and [Sertac Karaman](http://karaman.mit.edu/) at MIT. A video demonstration is available on [YouTube](https://youtu.be/vNIIT_M7x7Y). <p align="center"> <img src="http://www.mit.edu/~fcma/images/ICRA2018.png" alt="photo not available" width="50%" height="50%"> <img src="https://j.gifs.com/Z4qDow.gif" alt="photo not available" height="50%"> </p> This repo can be used for training and testing of - RGB (or grayscale image) based depth prediction - sparse depth based depth prediction - RGBd (i.e., both RGB and sparse depth) based depth prediction The original Torch implementation of the paper can be found [here](https://github.com/fangchangma/sparse-to-dense). ## Contents 0. [Requirements](#requirements) 0. [Training](#training) 0. [Testing](#testing) 0. [Trained Models](#trained-models) 0. [Benchmark](#benchmark) 0. [Citation](#citation) ## Requirements This code was tested with Python 3 and PyTorch 0.4.0. - Install [PyTorch](http://pytorch.org/) on a machine with CUDA GPU. - Install the [HDF5](https://en.wikipedia.org/wiki/Hierarchical_Data_Format) and other dependencies (files in our pre-processed datasets are in HDF5 formats). ```bash sudo apt-get update sudo apt-get install -y libhdf5-serial-dev hdf5-tools pip3 install h5py matplotlib imageio scikit-image opencv-python ``` - Download the preprocessed [NYU Depth V2](http://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html) and/or [KITTI Odometry](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) dataset in HDF5 formats, and place them under the `data` folder. The downloading process might take an hour or so. The NYU dataset requires 32G of storage space, and KITTI requires 81G. ```bash mkdir data; cd data wget http://datasets.lids.mit.edu/sparse-to-dense/data/nyudepthv2.tar.gz tar -xvf nyudepthv2.tar.gz && rm -f nyudepthv2.tar.gz wget http://datasets.lids.mit.edu/sparse-to-dense/data/kitti.tar.gz tar -xvf kitti.tar.gz && rm -f kitti.tar.gz cd .. ``` ## Training The training scripts come with several options, which can be listed with the `--help` flag. ```bash python3 main.py --help ``` For instance, run the following command to train a network with ResNet50 as the encoder, deconvolutions of kernel size 3 as the decoder, and both RGB and 100 random sparse depth samples as the input to the network. ```bash python3 main.py -a resnet50 -d deconv3 -m rgbd -s 100 --data nyudepthv2 ``` Training results will be saved under the `results` folder. To resume a previous training, run ```bash python3 main.py --resume [path_to_previous_model] ``` ## Testing To test the performance of a trained model without training, simply run main.py with the `-e` option. For instance, ```bash python3 main.py --evaluate [path_to_trained_model] ``` ## Trained Models A number of trained models is available [here](http://datasets.lids.mit.edu/sparse-to-dense.pytorch/results/). ## Benchmark The following numbers are from the original Torch repo. - Error metrics on NYU Depth v2: | RGB | rms | rel | delta1 | delta2 | delta3 | |-----------------------------|:-----:|:-----:|:-----:|:-----:|:-----:| | [Roy & Todorovic](http://web.engr.oregonstate.edu/~sinisa/research/publications/cvpr16_NRF.pdf) (_CVPR 2016_) | 0.744 | 0.187 | - | - | - | | [Eigen & Fergus](http://cs.nyu.edu/~deigen/dnl/) (_ICCV 2015_) | 0.641 | 0.158 | 76.9 | 95.0 | 98.8 | | [Laina et al](https://arxiv.org/pdf/1606.00373.pdf) (_3DV 2016_) | 0.573 | **0.127** | **81.1** | 95.3 | 98.8 | | Ours-RGB | **0.514** | 0.143 | 81.0 | **95.9** | **98.9** | | RGBd-#samples | rms | rel | delta1 | delta2 | delta3 | |-----------------------------|:-----:|:-----:|:-----:|:-----:|:-----:|
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