Summary of Helvipad: a Real-world Dataset For Omnidirectional Stereo Depth Estimation, by Mehdi Zayene et al.
Helvipad: A Real-World Dataset for Omnidirectional Stereo Depth Estimation
by Mehdi Zayene, Jannik Endres, Albias Havolli, Charles Corbière, Salim Cherkaoui, Alexandre Kontouli, Alexandre Alahi
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel dataset called Helvipad is introduced for omnidirectional stereo depth estimation, comprising 40K frames from various environments. The dataset includes accurate depth and disparity labels by projecting LiDAR sensor data onto equirectangular images. Additionally, an augmented training set with increased label density is provided using depth completion. Leading stereo depth estimation models are benchmarked on both standard and omnidirectional images, revealing that while recent methods perform well, there remains a significant challenge in accurately estimating depth in omnidirectional imaging. To address this, adaptations to stereo models are introduced, achieving improved performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new dataset for taking 3D pictures from all directions. This is hard because we don’t have enough good data to train computers to do it well. The researchers created a big dataset with over 40,000 images taken from different places and lighting conditions. They also made an extra set of training data by filling in gaps where the depth information was missing. Then they tested some computer programs that can already take 2D pictures from one direction and see how well they do on this new task. The results show that these programs aren’t very good at taking 3D pictures from all directions yet, so the researchers made some changes to help them do better. |
Keywords
» Artificial intelligence » Depth estimation
