Summary of Inspacetype: Reconsider Space Type in Indoor Monocular Depth Estimation, by Cho-ying Wu et al.
InSpaceType: Reconsider Space Type in Indoor Monocular Depth Estimation
by Cho-Ying Wu, Quankai Gao, Chin-Cheng Hsu, Te-Lin Wu, Jing-Wen Chen, Ulrich Neumann
First submitted to arxiv on: 24 Sep 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 study on indoor monocular depth estimation proposes a comprehensive analysis of existing methods’ performance across diverse space types, such as libraries or kitchens. The research addresses limitations in previous works by introducing InSpaceType, a high-quality RGBD dataset for general indoor environments. Twelve recent methods are benchmarked on this dataset, revealing significant performance imbalance concerning space types, indicating underlying biases. The study extends its analysis to four additional datasets, three mitigation approaches, and the ability to generalize to unseen space types. This work marks the first in-depth investigation of performance imbalance across space types for indoor monocular depth estimation, highlighting potential safety concerns and shedding light on ways to improve robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Indoor monocular depth estimation helps robots and computers understand our surroundings. Previous research focused on how well these systems worked overall. However, it’s important to know how they perform in different spaces, like a library or kitchen. A new dataset, InSpaceType, was created to test existing methods’ performance across various spaces. The study found that most methods struggled in some space types, which is concerning because this could affect their ability to safely navigate real-world environments. |
Keywords
* Artificial intelligence * Depth estimation
