- python 3.6.7
- tensorflow 1.8.0
On KITTI (test set)
CUDA_VISIBLE_DEVICES=3 python test.py --check weights-kitti-nyu-resizespp-100-v2/model-320000 --con configs/model-1s100.config --input_image figs/kitti_2011_09_26_drive_0001_sync_02_0000000012.jpg --max_depth 100
On NYU (test set)
CUDA_VISIBLE_DEVICES=3 python test.py --check weights-kitti-nyu-resizespp-100-v2/model-320000 --con configs/model-1s100.config --input_image figs/nyu_1449.jpg --max_depth 10
KITTI expected depth
KITTI entropy
NYU expected depth
NYU entropy
If you are interested in training the binary depth estimator, see the tf code for training. See README for details to train the model. Note the training code is very messy. It is recommended to start from Monodepth and use our code as a reference to modify the dataloader as well as the loss functions.
@InProceedings{yang2019inferring,
author = {Yang, Gengshan and Hu, Peiyun and Ramanan, Deva},
title = {Inferring distributions over depth from a single image},
booktitle = {2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2019}
}