A survey on RGB-D datasets

Lopes, Alexandre Gomes; Souza, Roberto; Pedrini, Hélio

doi:10.1016/j.cviu.2022.103489

Cited by 18 publications

(3 citation statements)

References 203 publications

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“…The general framework of our research work includes 3D spatial analysis, the temporal evolution of new housing estates and the deployment of smart cities, with scientific tools in artificial intelligence. Also, it seemed legitimate to us to take an interest in this portion of the city under construction to experiment with our approach which is the subject of this chapter: create an augmented reality scene model of the built environment through the combination of photogrammetry [76][77][78][79][80][81] and fuzzy modeling techniques.…”

Section: Urban Study Areamentioning

confidence: 99%

Fuzzy Photogrammetric Algorithm for City Built Environment Capturing into Urban Augmented Reality Model

Agbossou¹

2023

Advances in Fuzzy Logic Systems

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Cities are increasingly looking to become smarter and more resilient. Also, the use of computer vision takes a considerable place in the panoply of techniques and algorithms necessary for the 3D reconstruction of urban built environments. The models thus obtained make it possible to feed the logic of decision support and urban services thanks to the integration of augmented reality. This chapter describes and uses Fuzzy Cognitive Maps (FCM) as computing framework of visual features matching in augmented urban built environment modeling process. It is a combination of the achievements of the theory of fuzzy subsets and photogrammetry according to an algorithmic approach associated with the ARKit renderer. In this experimental research work, part of which is published in this chapter, the study area was confined to a portion of a housing estate and the data acquisition tools are in the domain of the public. The aim is the deployment of the algorithmic process to capture urban environments built in an augmented reality model and compute visual feature in stereovision within FCM framework. The comparison of the results obtained with our approach to two other well-known ones in the field, denotes the increased precision gain with a scalability factor.

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Section: Urban Study Areamentioning

confidence: 99%

Fuzzy Photogrammetric Algorithm for City Built Environment Capturing into Urban Augmented Reality Model

Agbossou¹

2023

Advances in Fuzzy Logic Systems

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Section: Urban Study Areamentioning

confidence: 99%

Advances in Fuzzy Logic Systems

2023

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“…Such issues are not limited to calibration and alignment procedures between cameras and depth sensors but are also related to unfilled depth maps captured with LiDAR devices and the wide range of possible scenarios. Even if many RGBD datasets have been proposed [11], most of them include less than 50K real-world samples such as NYU Depth v2 (NYU) [12] and KITTI [13] datasets. In contrast, millions of labeled samples are available for other computer vision tasks such as image classification (ImageNet [14]) and object detection (COCO [15]).…”

Section: Introductionmentioning

confidence: 99%

SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings

et al. 2022

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The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. Approaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for realtime inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoderdecoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving realtime performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.INDEX TERMS Computer vision, monocular depth estimation, fast-throughput, edge devices

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A survey on RGB-D datasets

Cited by 18 publications

References 203 publications

Fuzzy Photogrammetric Algorithm for City Built Environment Capturing into Urban Augmented Reality Model

Fuzzy Photogrammetric Algorithm for City Built Environment Capturing into Urban Augmented Reality Model

Advances in Fuzzy Logic Systems

SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings

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