Reconsidering indoor depth estimation across different spaces
Published on:
24 September 2023
Primary Category:
Computer Vision and Pattern Recognition
Paper Authors:
Cho-Ying Wu,
Quankai Gao,
Chin-Cheng Hsu,
Te-Lin Wu,
Jing-Wen Chen,
Ulrich Neumann
Bullets
Key Details
Collected new dataset with 12 indoor space types to study performance variance
Benchmarked 11 recent methods and revealed bias towards/against certain types
Analyzed 4 training datasets and enumerated biases in their space type coverage
Studied strategies to mitigate imbalance across types
Found generalization to unseen types difficult due to object/scale diversity
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AI generated summary
Reconsidering indoor depth estimation across different spaces
This paper investigates performance of indoor monocular depth estimation methods across different space types, such as kitchen, classroom, or lounge. It finds severe imbalance between space types for existing methods trained on NYUv2, indicating bias. A new dataset captures 12 space types. Analysis reveals strengths/weaknesses of different training sets. Unseen types are challenging. It emphasizes considering space types for robust deployment.
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