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A CNN based Depth, Optical Flow, Flow Uncertainty and Camera Pose Prediction pipeline

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ENG

ENG: End-to-end neural geometry for robust depth and pose estimation using CNNs

eng

Requirements

The code is tested on CUDA 9.0 + cudNN 7.4.2 + Python 3.5.2 + Tensorflow 1.9

Download the weights (requires wget)

./download_model.sh

Running

form the root dir of the project run:

python -m test_depth_flow_pose

Run Options

There are some configuration parameters, use the following command to print the help menu

python -m test_depth_flow_pose --help

Citing the Paper(s)

If you found this repository useful please cite the following:

@article{dharmasiri2018eng,
  title={ENG: End-to-end Neural Geometry for Robust Depth and Pose Estimation using CNNs},
  author={Dharmasiri, Thanuja and Spek, Andrew and Drummond, Tom},
  journal={arXiv preprint arXiv:1807.05705},
  year={2018}
}

Single Image Depth Prediction Results (KITTI 0-50m)

RMSE (m) Relabs Accuracy (δ) Accuracy (δ2) Accuracy (δ3)

3.284

0.092

90.6%

97.1%

98.9%

Single Image Depth Prediction Results (NYUv2 [using the indoor model] )

RMSE (m) Relabs Accuracy (δ) Accuracy (δ2) Accuracy (δ3)

0.478

0.111

87.2%

97.8%

99.5%

Pose Estimation (KITTI)

Sequence ATE (m) RPE (m) RPE (°)

9

16.55

0.047

0.128

10

9.846

0.039

0.138

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