KEYWORDS: Speckle, Speckle pattern, 3D modeling, Cameras, Clouds, Image fusion, Error analysis, High dynamic range imaging, 3D metrology, Fringe analysis
Speckle projection provides rich textures for correlation, and has been widely used in three-dimensional(3D) reconstruction. However, for objects with high dynamic range(HDR) surface, conventional uniform speckle projection usually causes over-exposure and over-dark regions simultaneously in the captured images, leading to miss-match and errors in 3D result. It is difficult to perform defect elimination via adjustment of speckle intensity globally or the camera exposure time. To tackle this problem, this paper proposed a novel adaptive speckle projection method to distinguish the appropriate projection intensity of specific parts in the speckle pattern, thereby avoiding over-exposure, while the dark regions not being affected. First, uniform intensity patterns of multiple gray-levels are projected onto the surface of testing object, the appropriate projection intensity at each pixel position in the camera coordinate system is calculated, and the saturated area in the captured image is marked. Then, a set of orthogonal fringe patterns are projected onto the testing object to establish the coordinates mapping relationship between the camera and the projection system, and the adaptive speckle pattern under the projection coordinate system is generated. Finally, the generated adaptive speckle pattern is used to scan the testing object, and the spatial-temporal correlation algorithm is used for 3D shape retrieval. Experimental results demonstrate feasibility of 3D shape reconstruction of HDR surfaces with the proposed method, and obvious advantages compared with the traditional methods in terms of reconstruction completion and measurement accuracy. Keywords: adaptive speckle, high dynamic range, coordinates mapping, spatial-temporal correlation, 3D reconstruction
A three-dimensional (3D) face profilometry based on facial landmarks and a multi-view system is proposed. Theoretically, at least three fringe patterns are required in phase-shifting profilometry, but the stereo relative phase pair obtained by a spatial unwrapping algorithm cannot be used for stereo matching directly because the starting points of phase unwrapping are not on the same fringe period order, so it will result in incorrect disparity. We proposed an innovative method that utilizes facial landmarks as prior knowledge to adjust the relative phase pair to the same phase reference for accurate stereo matching through a phase adjustment algorithm to determine the accurate fringe order difference. The three-step phase-shifting patterns are projected in a looping sequence, and each three-adjacent fringe stereo pair is treated as a group to obtain one frame of a 3D face, which means that the 3D face output of our proposed method can achieve the same frame rate as the camera. The accuracy evaluation and dynamic 3D facial expressions reconstruction verify the success of the proposed method.
Acquiring the three-dimensional (3-D) surface geometry of objects with a full-frame resolution is of great concern in many applications. This paper reports a 3-D measurement scheme based on single-frame pattern projection in the combination of random binary encoding and color encoding. Three random binary encoding patterns generated by a computer embedded in three channels of a color pattern lead to a color binary encoding pattern. Two color cameras with a stereo-vision arrangement simultaneously capture the measured scene under the proposed encoding structured illumination. From captured images, three encoding images are extracted and analyzed using the extended spatial–temporal correlation algorithm for 3-D reconstruction. Theoretical explanation and analysis concerning the encoding principle and reconstruction algorithm, followed by experiments for reconstructing 3-D geometry of stationary and dynamic scenes show the feasibility and practicality of the proposed method.
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