What's the correspondence between KITTI raw data and KITTI Odometry?

MonoRec

Paper | Video (CVPR) | Video (Reconstruction) | Project Page

This repository is the official implementation of the paper:

MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera

Felix Wimbauer*, Nan Yang*, Lukas Von Stumberg, Niclas Zeller and Daniel Cremers

CVPR 2021 (arXiv)

If you find our work useful, please consider citing our paper:

@InProceedings{wimbauer2020monorec,
  title = {{MonoRec}: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera},
  author = {Wimbauer, Felix and Yang, Nan and von Stumberg, Lukas and Zeller, Niclas and Cremers, Daniel},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

🏗️️ Setup

The conda environment for this project can be setup by running the following command:

conda env create -f environment.yml

🏃 Running the Example Script

We provide a sample from the KITTI Odometry test set and a script to run MonoRec on it in example/. To download the pretrained model and put it into the right place, run download_model.sh. You can manually do this by downloading the weights from here and unpacking the file to saved/checkpoints/monorec_depth_ref.pth. The example script will plot the keyframe, depth prediction and mask prediction.

cd example
python test_monorec.py

🗃️ Data

In all of our experiments we used the KITTI Odometry dataset for training. For additional evaluations, we used the KITTI, Oxford RobotCar, TUM Mono-VO and TUM RGB-D datasets. All datapaths can be specified in the respective configuration files. In our experiments, we put all datasets into a seperate folder ../data.

KITTI Odometry

To setup KITTI Odometry, download the color images and calibration files from the official website (around 145 GB). Instead of the given velodyne laser data files, we use the improved ground truth depth for evaluation, which can be downloaded from here.

Unzip the color images and calibration files into ../data. The lidar depth maps can be extracted into the given folder structure by running data_loader/scripts/preprocess_kitti_extract_annotated_depth.py.

For training and evaluation, we use the poses estimated by Deep Virtual Stereo Odometry (DVSO). They can be downloaded from here and should be placed under ../data/{kitti_path}/poses_dso. This folder structure is ensured when unpacking the zip file in the {kitti_path} directory.

To supplement the self-supervised training, we use sparse depth maps generated by Deep Virtual Stereo Odometry (DVSO) during the pose etimation. They can be downloaded from here and should be palced under ../data/{kitti_path}/sequences/{seq_num}/image_depth_sparse. This folder structure is ensured when unpacking the zip file in the {kitti_path} directory.

The auxiliary moving object masks can be downloaded from here. They should be placed under ../data/{kitti_path}/sequences/{seq_num}/mvobj_mask. This folder structure again is ensured when unpacking the zip file in the {kitti_path} directory.

Finally, for mask training, we also use index masks for the training data, which can be downloaded from here. They should be placed under ../data/{kitti_path}/sequences/{seq_num}/. This folder structure again is ensured when unpacking the zip file in the {kitti_path} directory.

Oxford RobotCar

To setup Oxford RobotCar, download the camera model files and the large sample from the official website. Code, as well as, camera extrinsics need to be downloaded from the official GitHub repository. Please move the content of the python folder to data_loaders/oxford_robotcar/. extrinsics/, models/ and sample/ need to be moved to ../data/oxford_robotcar/. Note that for poses we use the official visual odometry poses, which are not provided in the large sample. They need to be downloaded manually from the raw dataset and unpacked into the sample folder.

TUM Mono-VO

Unfortunately, TUM Mono-VO images are provided only in the original, distorted form. Therefore, they need to be undistorted first before fed into MonoRec. To obtain poses for the sequences, we run the publicly available version of Direct Sparse Odometry.

TUM RGB-D

The official sequences can be downloaded from the official website and need to be unpacked under ../data/tumrgbd/{sequence_name}. Note that our provided dataset implementation assumes intrinsics from fr3 sequences. Note that the data loader for this dataset also relies on the code from the Oxford Robotcar dataset.

🏋️ Training & Evaluation

This repository provides training and evaluation configurations to reproduce the results from the paper.

To train a model from scratch, first set the dataset_dir fields to the directory in which KITTI Odometry is located (default ../data/dataset). Then run the following commands in the given order:

python train.py --config configs/train/monorec/monorec_depth.json --options stereo                          # Depth Bootstrap
python train_monorec.py --config configs/train/monorec/monorec_mask.json --options stereo                   # Mask Bootstrap
python train_monorec.py --config configs/train/monorec/monorec_mask_ref.json --options mask_loss            # Mask Refinement
python train_monorec.py --config configs/train/monorec/monorec_depth_ref.json --options stereo stereo_repr  # Depth Refinement

The final model will be stored under saved/models/monorec_depth_ref/00/checkpoint.pth.

We also provide checkpoints for each training stage:

Training stage	Download
Depth Bootstrap	Link
Mask Bootstrap	Link
Mask Refinement	Link
Depth Refinement (final model)	Link

Run download_model.sh to download the final model. It will automatically get moved to saved/checkpoints.

To reproduce the evaluation results on different datasets, run the following commands:

python evaluate.py --config configs/evaluate/eval_monorec.json        # KITTI Odometry
python evaluate.py --config configs/evaluate/eval_monorec_oxrc.json   # Oxford Robotcar

☁️ Pointclouds

To reproduce the pointclouds depicted in the paper and video, use the following commands:

python create_pointcloud.py --config configs/test/pointcloud_monorec.json       # KITTI Odometry
python create_pointcloud.py --config configs/test/pointcloud_monorec_oxrc.json  # Oxford Robotcar
python create_pointcloud.py --config configs/test/pointcloud_monorec_tmvo.json  # TUM Mono-VO

	_intrinsics = torch.tensor(
	[[535.4, 0, 320.1, 0],
	[0, 539.2, 247.6, 0],
	[0, 0, 1, 0],
	[0, 0, 0, 1]
	], dtype=torch.float32)

	if self.annotated_lidar and self.lidar_depth:
	extra_frames = max(extra_frames, 10)
	self._offset = max(self._offset, 5)

brummi / monorec Goto Github PK

monorec's Introduction

MonoRec

🏗️️ Setup

🏃 Running the Example Script

🗃️ Data

KITTI Odometry

Oxford RobotCar

TUM Mono-VO

TUM RGB-D

🏋️ Training & Evaluation

☁️ Pointclouds

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