VDW : Video Depth in the Wild


The largest natural-scene video depth dataset with the most diverse video scenes

GitHub

Paper

Project

Video

Construction

Overall Description

Current video depth datasets are limited in both diversity and volume. To compensate for the data shortage and boost the performance of learning-based video depth models, we elaborate a large-scale natural-scene dataset, Video Depth in the Wild (VDW). To the best of our knowledge, our VDW dataset is currently the largest video depth dataset with the most diverse video scenes.

Raw Video Acquisition

We collect stereo videos from four data sources: movies, animations, documentaries, and web videos. A total of 60 animations, documentaries, and movies in Blu-ray format are collected. We also crawl 739 web stereo videos from YouTube with the keywords such as “stereoscopic” and “stereo”. To balance the realism and diversity, only 24 movies, animations, and documentaries are retained.

Data Pre-processing

Having obtained the raw videos, we use FFmpeg and PySceneDetect to split all the videos into 104,582 sequences. We manually check and remove the duplicated, chaotic, and blur scenes. Videos that are wrongly split by the scene detect tools are also removed. Finally, we reserve 32,405 videos with more than six million frames for disparity annotations.

Sky Segmentation

As sky is infinitely far, pixels in sky regions should be set to the minimum value in disparity maps. We generate sky masks in a model ensemble manner. Each frame along with its horizontally flipped copy are fed into two semantic segmentation models SegFormer and Mask2Former, which yields four sky masks in total. A pixel is considered as the sky when it is positive in more than two predicted sky masks. Besides, we also fill the connected regions with less than 50 pixels to further remove noisy holes in sky masks.

Disparity Annotation

We adopt the optical flow model GMFlow to generate the ground truth disparity of the left- and right-eye views. The estimated optical flow is bidirectional. We perform a consistency check between the optical flow pairs to obtain the valid masks for training. We adopt an adaptive consistency threshold for each pixel. The ground truth of each video is normalized by its minimum and maximum disparity. The disparity value is discretized into 65,535 intervals.

Invalid Sample Filtering

We further filter the videos that are not qualified for our dataset. According to optical flow and valid masks, samples with the following three conditions are removed: 1) more than 30% of pixels in the consistency masks are invalid; 2) more than 10% of pixels have vertical disparity larger than two pixels; 3) the average range of horizontal disparity is less than 15 pixels. Then, we manually check all the videos along with their corresponding ground truth, and remove the samples with obvious errors. Finally, we retain 14,203 videos with 2,237,320 frames in VDW dataset.

Statistics

  • Dataset Comparisons
  • Objective Statistics
  • Train / Test Split
  • VDW dataset has larger numbers of video scenes. Compared with the closed-domain datasets, VDW is not restricted to a certain scene, which is more helpful to train a robust video depth model. For the natural-scene datasets, VDW has more than ten times the number of videos as the previous largest dataset WSVD. It is also worth noticing that our VDW dataset has higher resolutions. We only collect videos over 1080p and crop them to 1880 × 800 to remove black bars and subtitles.

    Comparisons
  • VDW contains 14,203 videos with 2,237,320 frames. The total data collection and processing takes over six months and about 4,000 man-hours. To verify the diversity of scenes and entities in our dataset, we conduct semantic segmentation by Mask2Former trained on ADE20k. All the 150 categories are covered in our dataset, and each category can be found in at least 50 videos. The five categories that present most frequently are person (97.2%), wall (89.1%), floor (63.5%), ceiling (46.5%), and tree (42.3%).

    Statistics
  • We randomly adopts 90 videos with 12,622 frames as test set. Test videos adopt different data sources from training data, i.e., different movies, web videos, or animations. VDW not only alleviates the data shortage for learning-based approaches, but also serves as a comprehensive benchmark for video depth.

    VDW Training Set

    VDW Test Set

Download and Usage

We have released the VDW dataset under strict conditions. We must ensure that the releasing of VDW dataset won’t violate any copyright requirements and regulations. Thus, we will not release any video frames or the derived data. Instead, we provide the meta data and detailed toolkits, which can be used to reproduce VDW or generate your own dataset. All our released meta data and toolkits are licensed under CC BY-NC-SA 4.0, which can only be used for academic and research purposes. The VDW dataset cannot be adopted for any commercial usage.

The VDW test set is with 90 videos and 12622 frames, while the VDW training set contains 14203 videos with over 2 million frames (8TB on hard drive). We also provide a VDW demo set with two sequences. Users could leverage the VDW official toolkits with demo sequences to learn about our data processing pipeline.

The release of VDW dataset will be divided into two parts: (1) Downloading for metadata; (2) Toolkits for data usage or constructions.

Updates

  1. [2023.09.09] The VDW official website is released, along with the VDW official mailbox.
  2. [2023.01.22] The metadata, usage, and evaluation code of the VDW test set.
  3. [2024.06.03] The VDW official toolkits to reproduce and generate.

Download For Metadata

We present VDW metadata for downloading. Downloading our metadata represents your agreement to our license and conditions. For the metadata of each sequence, we provide movie name, IMDB numbers, resolutions, movie durations (second), sequence start time, end time, and cropping area (Four coordinate values from the upper left corner to the lower right corner). The downloading of metadata is presented as follows. The generation and inspections of the meta data for the large training set take lots of time and work. We will release that part once we are ready.

For More Information

Our VDW dataset cannot be used for any commercial purposes. If you need more help or information to generate disparity, you can contact the VDW official email: vdw.dataset@gmail.com. You can use the template to specify your requirements. Your name, institution, purposes, and agreements to License should be included. We will send you feedback on 3-5 weekdays.

License

The releasing of VDW dataset is under strict conditions. Our VDW dataset cannot be used or distributed for any commercial purposes. It can only be used for academic and research purposes. Thus, VDW dataset is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Under this license, if you want to modify VDW or generate new data from VDW dataset (e.g., generating defocus maps for bokeh rendering), the releasing of your new data should be licensed under the same CC BY-NC-SA 4.0.

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The VDW Dataset is proposed by the ICCV2023 paper
“Neural Video Depth Stabilizer”


Thanks all the authors for their contribution to our work,
especially appreciate Jiaqi Li and Zihao Huang for their help in VDW dataset constructions.


Institution: 1Huazhong University of Science and Technology
2Adobe Research    3Nanyang Technological University


Please refer to Download and Usage for using our VDW dataset. If you have any questions about the dataset, feel free to contact us by the VDW official email.