# A High Resolution Multi-exposure Stereoscopic Image & Video Database of Natural Scenes

arXiv: https://arxiv.org/abs/2206.11095 · HTML: https://mlsanity.com/paper/2206.11095/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 86 references that are in the map.
- First posted on arXiv 22 Jun 2022.

## Builds on (most cited first, 16 of 16)

- [Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction](https://mlsanity.com/paper/1803.03893/) (2018)
- [DPSNet: End-to-end Deep Plane Sweep Stereo](https://mlsanity.com/paper/1905.00538/) (2019)
- [Deep High Dynamic Range Imaging with Large Foreground Motions](https://mlsanity.com/paper/1711.08937/) (2017)
- [DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs](https://mlsanity.com/paper/1712.07384/) (2017)
- [Don't Forget The Past: Recurrent Depth Estimation from Monocular Video](https://mlsanity.com/paper/2001.02613/) (2020)
- [Exploiting temporal consistency for real-time video depth estimation](https://mlsanity.com/paper/1908.03706/) (2019)
- [HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset](https://mlsanity.com/paper/2103.14943/) (2021)
- [Deep Learning for HDR Imaging: State-of-the-Art and Future Trends](https://mlsanity.com/paper/2110.10394/) (2021)
- [Image Correction via Deep Reciprocating HDR Transformation](https://mlsanity.com/paper/1804.04371/) (2018)
- [Deep Tone Mapping Operator for High Dynamic Range Images](https://mlsanity.com/paper/1908.04197/) (2019)
- [ROBI: A Multi-View Dataset for Reflective Objects in Robotic Bin-Picking](https://mlsanity.com/paper/2105.04112/) (2021)
- [Open-World Stereo Video Matching with Deep RNN](https://mlsanity.com/paper/1808.03959/) (2018)
- [HDR Video Reconstruction with Tri-Exposure Quad-Bayer Sensors](https://mlsanity.com/paper/2103.10982/) (2021)
- [MCL-3D: a database for stereoscopic image quality assessment using 2D-image-plus-depth source](https://mlsanity.com/paper/1405.1403/) (2014)
- [Benchmark 3D eye-tracking dataset for visual saliency prediction on stereoscopic 3D video](https://mlsanity.com/paper/1803.04845/) (2018)
- [Introducing A Public Stereoscopic 3D High Dynamic Range (SHDR) Video Database](https://mlsanity.com/paper/1803.04847/) (2018)

## Abstract (arXiv)

Immersive displays such as VR headsets, AR glasses, Multiview displays, Free point televisions have emerged as a new class of display technologies in recent years, offering a better visual experience and viewer engagement as compared to conventional displays. With the evolution of 3D video and display technologies, the consumer market for High Dynamic Range (HDR) cameras and displays is quickly growing. The lack of appropriate experimental data is a critical hindrance for the development of primary research efforts in the field of 3D HDR video technology. Also, the unavailability of sufficient real world multi-exposure experimental dataset is a major bottleneck for HDR imaging research, thereby limiting the quality of experience (QoE) for the viewers. In this paper, we introduce a diversified stereoscopic multi-exposure dataset captured within the campus of Indian Institute of Technology Madras, which is home to a diverse flora and fauna. The dataset is captured using ZED stereoscopic camera and provides intricate scenes of outdoor locations such as gardens, roadside views, festival venues, buildings and indoor locations such as academic and residential areas. The proposed dataset accommodates wide depth range, complex depth structure, complicate object movement, illumination variations, rich color dynamics, texture discrepancy in addition to significant randomness introduced by moving camera and background motion. The proposed dataset is made publicly available to the research community. Furthermore, the procedure for capturing, aligning and calibrating multi-exposure stereo videos and images is described in detail. Finally, we have discussed the progress, challenges, potential use cases and future research opportunities with respect to HDR imaging, depth estimation, consistent tone mapping and 3D HDR coding.