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Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals
| Date | Stars |
|---|---|
| 2026-07-24 | 334 |
| 2026-07-25 | 334 |
| 2026-07-28 | 334 |
| 2026-07-30 | 334 |
| 2026-08-06 | 334 |
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# LapDepth-release [](https://paperswithcode.com/sota/monocular-depth-estimation-on-kitti-eigen?p=monocular-depth-estimation-using-laplacian) [](https://paperswithcode.com/sota/monocular-depth-estimation-on-nyu-depth-v2?p=monocular-depth-estimation-using-laplacian) This repository is a Pytorch implementation of the paper [**"Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals"**](https://ieeexplore.ieee.org/document/9316778) <p align="center"><img src='https://github.com/tjqansthd/LapDepth-release/blob/master/example/FIG1.png' width=800></p> Minsoo Song, Seokjae Lim, and [Wonjun Kim*](https://sites.google.com/view/dcvl) IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) ## Video presentation [](https://youtu.be/mbK4ysUX8oo) ## Requirements * Python >= 3.7 * Pytorch >= 1.6.0 * Ubuntu 16.04 * CUDA 9.2 * cuDNN (if CUDA available) some other packages: geffnet, path, IPython, blessings, progressbar ## Pretrained models You can download pre-trained model * [Trained with KITTI](https://drive.google.com/file/d/10Fsw3KbhiKj-rRkoIesghSPCn84TYY5P/view?usp=sharing) - batch 16, SyncBatchNorm, data loss | cap | a1 | a2 | a3 | Abs Rel | Sq Rel | RMSE | RMSE log | | :---: | :---: | :---: | :---: | :-----: | :----: | :---: | :------: | | 0-80m | 0.965 | 0.995 | 0.999 | 0.059 | 0.201 | 2.397 | 0.090 | | cap | a1 | a2 | a3 | Abs Rel | Sq Rel | RMSE | RMSE log | | :---: | :---: | :---: | :---: | :-----: | :----: | :---: | :------: | | 0-50m | 0.970 | 0.996 | 0.999 | 0.057 | 0.155 | 1.788 | 0.085 | * [Trained with KITTI](https://drive.google.com/file/d/1Xxxo3Zw4kVwBw43i6akQqjX2Go8vdP4H/view?usp=sharing) - batch 16, GroupNorm, data loss + gradient loss | cap | a1 | a2 | a3 | Abs Rel | Sq Rel | RMSE | RMSE log | | :---: | :---: | :---: | :---: | :-----: | :----: | :---: | :------: | | 0-80m | 0.961 | 0.994 | 0.999 | 0.059 | 0.209 | 2.489 | 0.091 | | cap | a1 | a2 | a3 | Abs Rel | Sq Rel | RMSE | RMSE log | | :---: | :---: | :---: | :---: | :-----: | :----: | :---: | :------: | | 0-50m | 0.968 | 0.996 | 0.999 | 0.057 | 0.155 | 1.807 | 0.085 | * [Trained with NYU Depth V2](https://drive.google.com/file/d/13WyHCmQINyzprCerkOBT_Pf_W-PbWzBi/view?usp=sharing) - batch 16, SyncBatchNorm, data loss | cap | a1 | a2 | a3 | Abs Rel | log10 | RMSE | RMSE log | | :---: | :---: | :---: | :---: | :-----: | :---: | :---: | :------: | | 0-10m | 0.895 | 0.983 | 0.996 | 0.105 | 0.045 | 0.384 | 0.135 | ## Demo images (Single Test Image Prediction) Make sure you download the pre-trained model and placed it in the './pretrained/' directory before running the demo. Demo Command Line: ```bash ############### Example of argument usage ##################### ## Running demo using a specified image (jpg or png) python demo.py --model_dir ./pretrained/LDRN_KITTI_ResNext101_pretrained_data.pkl --img_dir ./your/file/path/filename --pretrained KITTI --cuda --gpu_num 0 python demo.py --model_dir ./pretrained/LDRN_NYU_ResNext101_pretrained_data.pkl --img_dir ./your/file/path/filename --pretrained NYU --cuda --gpu_num 0 # output image name => 'out_' + filename ## Running demo using a whole folder of images python demo.py --model_dir ./pretrained/LDRN_KITTI_ResNext101_pretrained_data.pkl --img_folder_dir ./your/folder/path/folder_name --pretrained KITTI --cuda --gpu_num 0 # output folder name => 'out_' + folder_name ``` If you are using a model pre-trained from KITTI, insert **'`--pretrain
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matched fp:5f2e8b00db26d419, topic:computer-vision, desc:depth estimation, readme:depth estimation
matched fp:5f2e8b00db26d419, topic:deep-learning