Depth Completion with Anisotropic Metric, Convolutional Stages, and Infinity Laplacian

التفاصيل البيبلوغرافية
العنوان:	Depth Completion with Anisotropic Metric, Convolutional Stages, and Infinity Laplacian
المؤلفون:	Vanel Lazcano, Felipe Calderero
المصدر:	Applied Sciences, Vol 14, Iss 11, p 4514 (2024)
بيانات النشر:	MDPI AG
سنة النشر:	2024
المجموعة:	Directory of Open Access Journals: DOAJ Articles
مصطلحات موضوعية:	depth completion, depth map interpolation, infinity Laplacian, convolutional neural networks, Texture+Structure decomposition, anisotropic metric, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف:	Depth map estimation is crucial for a wide range of applications. Unfortunately, it often presents missing or unreliable data. The objective of depth completion is to fill in the “holes” in a depth map by propagating the depth information using guidance from other sources of information, such as color. Nowadays, classical image processing methods have been outperformed by deep learning techniques. Nevertheless, these approaches require a significantly large number of images and enormous computing power for training. This fact limits their usability and makes them not the best solution in some resource-constrained environments. Therefore, this paper investigates three simple hybrid models for depth completion. We explore a hybrid pipeline that combines a very efficient and powerful interpolator (infinity Laplacian or AMLE) and a series of convolutional stages. The contributions of this article are (i) the use a Texture+Structuredecomposition as a pre-filter stage; (ii) an objective evaluation with three different approaches using KITTI and NYU_V2 data sets; (iii) the use of an anisotropic metric as a mechanism to improve interpolation; and iv) the inclusion of an ablation test. The main conclusions of this work are that using an anisotropic metric improves model performance, and the ablation test demonstrates that the model’s final stage is a critical component in the pipeline; its suppression leads to an approximate 4% increase in M S E . We also show that our model outperforms state-of-the-art alternatives with similar levels of complexity.
نوع الوثيقة:	article in journal/newspaper
اللغة:	English
تدمد:	2076-3417
Relation:	https://www.mdpi.com/2076-3417/14/11/4514; https://doaj.org/toc/2076-3417; https://doaj.org/article/1de46a18df2b41b68601646ba8f1561a
DOI:	10.3390/app14114514
الاتاحة:	https://doi.org/10.3390/app14114514 https://doaj.org/article/1de46a18df2b41b68601646ba8f1561a
رقم الانضمام:	edsbas.59014277
قاعدة البيانات:	BASE

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الوصف
تدمد:	20763417
DOI:	10.3390/app14114514