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Indoor monocular depth estimation has attracted increasing research interest. Most previous works have been focusing on methodology, primarily experimenting with NYU-Depth-V2 (NYUv2) Dataset, and only concentrated on the overall performance over the test set. However, little is known regarding robustness and generalization when it comes to applying monocular depth estimation methods to real-world scenarios where highly varying and diverse functional \textit{space types} are present such as library or kitchen. A study for performance breakdown into space types is essential to realize a pretrained model's performance variance. To facilitate our investigation for robustness and address limitations of previous works, we collect InSpaceType, a high-quality and high-resolution RGBD dataset for general indoor environments. We benchmark 12 recent methods on InSpaceType and find they severely suffer from performance imbalance concerning space types, which reveals their underlying bias. We extend our analysis to 4 other datasets, 3 mitigation approaches, and the ability to generalize to unseen space types. Our work marks the first in-depth investigation of performance imbalance across space types for indoor monocular depth estimation, drawing attention to potential safety concerns for model deployment without considering space types, and further shedding light on potential ways to improve robustness. See \url{https://depthcomputation.github.io/DepthPublic} for data and the supplementary document. The benchmark list on the GitHub project page keeps updates for the lastest monocular depth estimation methods., Comment: Add Depth-Anything

Li-ping Huang,1* Guo-qi Wang,2* Zi-shan Jia,1 Jing-wen Chen,1 Gang Wang,1 Xing-lin Wang1   1Department of Physical Therapy, 2Department of Orthopedics, Chinese PLA General Hospital, Beijing, People’s Republic of China *These authors contributed equally to this work Background and objective: The onset and progression of pathological scarring involves multiple cytokines and complex mechanisms. However, hyperplasia of fibroblasts and neovascularization plays important roles, which can be inhibited by paclitaxel. The aim of this study was to investigate the efficacy of paclitaxel in the treatment of hypertrophic scars on rabbit ears. Methods: Rabbit ear models of hypertrophic scars were established to observe the therapeutic effects of paclitaxel at different concentrations (12 mg/L, 24 mg/L, 48 mg/L, 96 mg/L, 18 mg/L, 54 mg/L, 162 mg/L, 486 mg/L, 30 mg/L, 150 mg/L, 750 mg/L, 3,750 mg/L). The outcome measures included hypertrophic index (HI), density of fibroblasts, density of collagenous fibers, and microvessel density. Results: In comparison with the control group, the concentrations of 96 mg/L, 150 mg/L, and 162 mg/L significantly reduce the formation of hypertrophic scars in the rabbit ear models. However, local necrosis was found in the rabbit ear models treated with paclitaxel solution >400 mg/L. Conclusion: Paclitaxel has strong inhibitory effects on the hyperplasia of fibroblasts, deposition of collagen, and microangiogenesis in hypertrophic scars on rabbit ears within the concentration range from 48 mg/L to 162 mg/L, without causing local necrosis. Keywords: hypertrophic scar, paclitaxel, rabbit ear model