To achieve one of the tasks required for disaster response robots, this paper proposes a method for locating 3d structured switches' points to be pressed by the robot in disaster sites using RGBdimages acquired b...
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Volumetric examinations of the aorta are nowadays of crucial importance for the management of critical pathologies such as aortic dissection, aortic aneurism, and other pathologies, which affect the morphology of the ...
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The miniaturization of 3ddepth camera systems to reduce cost and power consumption is essential for their application in electrical devices that are trending toward smaller sizes (such as smartphones and unmanned aer...
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The miniaturization of 3ddepth camera systems to reduce cost and power consumption is essential for their application in electrical devices that are trending toward smaller sizes (such as smartphones and unmanned aerial systems) and in other applications that cannot be realized via conventional approaches. Currently, equipment exists for a wide range of depth-sensing devices, including stereo vision, structured light, and time-of-flight. This paper reports on a miniaturized3ddepth camera based on a light field camera (LFC) configured with a single aperture and a micro-lens array (MLA). The single aperture and each micro-lens of the MLA serve as multi-camera systems for 3d surface imaging. To overcome the optical alignment challenge in the miniaturized LFC system, the MLA was designed to focus by attaching it to an image sensor. Theoretical analysis of the optical parameters was performed using optical simulation based on Monte Carlo ray tracing to find the valid optical parameters for miniaturized3d camera systems. Moreover, we demonstrated multi-viewpoint imageacquisition via a miniaturized3d camera module integrated into a smartphone.
在运动恢复结构(Structure From Motion,SFM)技术的实际应用中,不同数量影像数据集的SFM重建精度和效率通常存在差异。为了研究影像数量对SFM重建模型精度的影响,为三维重建应用中的影像数据采集与选取提供参考依据,利用手持数码相机对...
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在运动恢复结构(Structure From Motion,SFM)技术的实际应用中,不同数量影像数据集的SFM重建精度和效率通常存在差异。为了研究影像数量对SFM重建模型精度的影响,为三维重建应用中的影像数据采集与选取提供参考依据,利用手持数码相机对目标物体进行数据采集,选取不同数量的影像执行SFM点云和网格模型的重建,并将各组SFM三维模型与激光扫描模型进行模型精确度、噪声和完整度的对比分析。实验结果表明,影像数量与SFM三维模型的精度之间具有一定的相关性,影像数量越多,重建模型的完整度越高,但受SFM特征点匹配误差、配准误差以及影像中多余场景的影响,影像数量的增加会导致模型精确度的降低以及噪声程度的加重。在实际的工程应用中,需要深入分析生产三维模型的特点和需求,选取合适影像数量的数据集进行SFM三维重建。
Stereoscopic image quality evaluation is extremely significance as a performance evaluator of modern 3ddisplaytechnology. due to the complexity of human visual system (HVS) and the incomprehensive study of stereosco...
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Stereoscopic image quality evaluation is extremely significance as a performance evaluator of modern 3ddisplaytechnology. due to the complexity of human visual system (HVS) and the incomprehensive study of stereoscopic perception of human eyes, stereoscopic image quality assessment (SIQA) is still a challenging task. In this paper, combining binocular characteristics, we propose an efficient no-reference stereoscopic image quality assessment according to binocular adding and subtracting channels. distinguished from other SIQA methods, which pay attention to complex binocular visual properties, the visual information which is closely bound up with imagedistortion from adding and subtracting channels to describe ocular dominance (alternate name binocularity) is proposed. To estimate the contribution of each channel in SIQA, a dynamic weighting system is proposed for binocular fusion according to local energy. Furthermore, quality awareness features based on multi-scale and multi-orientation are extracted to describe visual degradation. Comparing with existing methods, experimental results on public 3dimagedatabases demonstrate the proposed framework achieves high consistent with the subjective quality scores. (C) 2018 Elsevier B.V. All rights reserved.
This study focused on a polarization three-dimensional imaging technology under the natural scene. By solving the problem of diffuse reflection information missing, highlighted areas three-dimensional imaging is possi...
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As the computing power of handhelddevices grows, there has been increasing interest in the capture of depth information to enable a variety of photographic applications. However, under low-light conditions, most devi...
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As the computing power of handhelddevices grows, there has been increasing interest in the capture of depth information to enable a variety of photographic applications. However, under low-light conditions, most devices still suffer from low imaging quality and inaccurate depth acquisition. To address the problem, we present a robust depth estimation method from a short burst shot with varied intensity (i.e., auto-exposure bracketing) and/or strong noise (i.e., high ISO). Our key idea synergistically combines deep convolutional neural networks with a geometric understanding of the scene. We introduce a geometric transformation between optical flow anddepth tailored for burst images, enabling our learning-based multi-view stereo matching to be performed effectively. We then describe our depth estimation pipeline that incorporates this geometric transformation into our residual-flow network. It allows our framework to produce an accurate depth map even with a bracketedimage sequence. We demonstrate that our method outperforms the state-of-the-art methods for various datasets captured by a smartphone and a dSLR camera. Moreover, we show that the estimateddepth is applicable for image quality enhancement and photographic editing.
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