Summary of Firestereo: Forest Infrared Stereo Dataset For Uas Depth Perception in Visually Degraded Environments, by Devansh Dhrafani et al.
FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments
by Devansh Dhrafani, Yifei Liu, Andrew Jong, Ukcheol Shin, Yao He, Tyler Harp, Yaoyu Hu, Jean Oh, Sebastian Scherer
First submitted to arxiv on: 12 Sep 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 This paper presents a crucial step towards developing autonomous aerial systems that can operate effectively in visually-degraded environments, such as smoke-filled or rainy conditions. The authors focus on stereo thermal depth perception, which uses infrared radiation captured by thermal imaging cameras to create 3D models of the environment. A major challenge in this area is the lack of large-scale datasets, but the paper addresses this issue by introducing a new dataset consisting of stereo thermal images, LiDAR data, and ground truth depth maps captured in various settings. The authors benchmark several state-of-the-art stereo depth estimation algorithms on this dataset and show that models trained on their data generalize well to unseen conditions, including smoky environments. This work has the potential to enhance robotic perception in disaster scenarios, enabling robots to explore and operate in areas previously inaccessible. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous aerial systems need to be able to navigate through smoke-filled or rainy environments, which is a big challenge. The problem is that we don’t have enough data to train machines to do this. This paper tries to fix that by creating a new dataset of thermal images and other information that can help machines understand the world around them. They test some existing algorithms on this dataset and show that they work well even in smoky conditions, which is important for using robots in disaster situations. |
Keywords
» Artificial intelligence » Depth estimation
