A newer code release is available here, which supports learning with highly dynamic scenes without any semantic information.

This repository contains a preliminary release of code for the paper bearing the title above, published at ICCV 2019. The code is based on the Struct2depth [repository] (https://github.com/tensorflow/models/tree/master/research/struct2depth) (see the respective paper here), and utilizes the same data format.

This release supports training a depth and motion prediciton model, with either learned or specified camera intrinsics. The motion model produces 6 degrees of freedom of camera motion, and a dense translation vector field for every pixel in the scene. As an input, the code needs triplets of RGB frames with possibly-moving objects masked out.

Sample command line:

python -m depth_from_video_in_the_wild.train \
   --checkpoint_dir=$MY_CHECKPOINT_DIR \
   --data_dir=$MY_DATA_DIR \
   --imagenet_ckpt=$MY_IMAGENET_CHECKPOINT

MY_CHECKPOINT_DIR is where the trained model checkpoints are to be saved.

MY_DATA_DIR is where the training data (in Struct2depth's format) is stored. The data_example folder contains a single training example expressed in this format.

MY_IMAGENET_CHECKPOINT is a path to a pretreained ImageNet checkpoint to intialize the encoder of the depth prediction model.

On Cityscapes we used the default batch size (4), for KITTI we used a batch size of 16 (add --batch_size=16 to the training command).

A command line for running a single training step on the single example in data_example (for testing):

python -m depth_from_video_in_the_wild.train \
  --data_dir=depth_from_video_in_the_wild/data_example \
  --checkpoint_dir=/tmp/my_experiment --train_steps=1

To use the given intrinsics instead of learning them, add --nolearn_intrinsics to the coomand.

The table below provides checkpoints trained on Cityscapes, KITTI and their mixture, with the respective Absolute Relative depth error metrics. The metrics slightly differ from the results in Table A3 in the paper because for the latter we averaged the metrics over multiple checkpoints, whereas in the table below the metrics relate to a specific checkpoint. All checkpoints were harvested after training on nearly 4M images (since the datasets are much smaller than 4M, this of course means multiple epochs).

The command for generating a trajectory from a checkpoint given an odometry test set is:

python -m depth_from_video_in_the_wild.trajectory_inference \
  --checkpoint_path=$YOUR_CHECKPOINT_PATH \
  --odometry_test_set_dir=$DIRECTORY_WHERE_YOU_STORE_THE_ODOMETRY_TEST_SET \
  --output_dir=$DIRECTORY_WHERE_THE_TRAJECTORIES_WILL_BE_SAVED \
  --alsologtostderr

We observed that odometry generally took longer to converge. The table below lists the checkpoints used to evaluate odometry on in the paper. All checkpoints were trained on KITTI. The training batch size was 16, and the learning rate and number of training steps is given in the table.

The code for generating "Learned & corrected" is not yet publically available.

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The YouTube8M website provides the instructions for mapping them you YouTube IDs. Two consecutive frames were sampled off of each video every second.

The checkpoint used to obtain the results in Table 4 in the paper is given here. It was trained on all the "Machine Hall" sequences jointly, with learned intrinsics. In addition, we trained a model on each sequence separatelty, the results were used to obtain the numbers in Table 5. The respective checkpoints are given in the table below. All models in this section were trained with a resolution of 256x384.