A method for registering an imaging detector to a surface projects and records a sequence having a first sparse pattern of lines followed by a second sparse pattern of lines. A first subset of positions receives lines from both first and second sparse patterns corresponding to a first label. A second subset of positions receives only lines from the first sparse pattern corresponding to a second label. A third subset of positions receives only lines from the second sparse pattern corresponding to a third label. The first, second, and third labels are decoded and each member element of the first, second, and third subsets of positions registered to the imaging detector according to the decoded labels. One or more dense patterns of lines positionally correlated with registered member elements of the decoded labels are projected and recorded. An image of the surface contour is formed according to the recorded pattern.

A 3D scanner for recording the 3D topography of an object, the 3D scanner including: a projector unit configured for projecting a structured beam of probe light onto the object; an imaging unit arranged to acquire 2D images of the object when the object is illuminated by the structured probe light beam; and an actuator unit arranged to control the position of the structured probe light beam at the object by rotating a movable portion of the projector unit around a pivoting axis, the actuator unit including a rotation motor including or arranged to drive a wheel, where the surface of the wheel operatively coupled to the movable portion of the projector unit has a radial distance from the axis of the rotation motor which changes with the rotation.

An apparatus includes a tool body and a structured illumination device attached to the tool body, wherein the structured illumination device includes a light source and a light filter mask to generate a light pattern. The apparatus also includes a subsurface camera system attached to the tool body, wherein the subsurface camera system comprises a plurality of cameras. The apparatus also includes a processor and a machine-readable medium having program code executable by the processor to cause the apparatus to acquire an image of a feature using the subsurface camera system and determine a three-dimensional position of the feature based on the image, wherein the feature is illuminated by the light pattern.

A distance measuring device includes a light output unit outputting a linear laser beam, a light scanning unit including a mirror that reflects the laser beam from the light output unit while swinging and generating a pattern light on an object, a light detection unit placed in a position equal to or less than 90% of maximum swing amplitude of the mirror, and receiving the light reflected by the mirror and outputting a light reception signal, an imaging unit imaging the pattern light, a measuring unit measuring a distance to the object based on a result of imaging by the imaging unit, and a control unit controlling generation of the pattern light based on the light reception signal.

The invention discloses a deep learning-based temporal phase unwrapping method for fringe projection profilometry. First, four sets of three-step phase-shifting fringe patterns with different frequencies (including 1, 8, 32, and 64) are projected to the tested objects. The three-step phase-shifting fringe images acquired by the camera are processed to obtain the wrapped phase map using a three-step phase-shifting algorithm. Then, a multi-frequency temporal phase unwrapping (MF-TPU) algorithm is used to unwrap the wrapped phase map to obtain a fringe order map of the high-frequency phase with 64 periods. A residual convolutional neural network is built, and its input data are set to be the wrapped phase maps with frequencies of 1 and 64, and the output data are set to be the fringe order map of the high-frequency phase with 64 periods. Finally, the training dataset and the validation dataset are built to train and validate the network. The network makes predictions on the test dataset to output the fringe order map of the high-frequency phase with 64 periods. The invention exploits a deep learning method to unwrap a wrapped phase map with a frequency of 64 using a wrapped phase map with a frequency of 1 and obtain an absolute phase map with fewer phase errors and higher accuracy.

An appearance inspection device (three-dimensional measuring device) includes a first measurement unit configured to measure three-dimensional information by a phase shift method, a second measurement unit configured to measure three-dimensional information by an optical cutting method, and a control device configured or programmed to acquire three-dimensional information on a measurement target based on measurement results of both the first measurement unit and the second measurement unit.

An imaging processing part 131 executes first imaging processing of causing a first illuminating part to emit structured light from a first direction to a measurement target, causing an imaging part 120 to generate image data, and causing a buffer memory 133 to store therein the generated image data. A computing processing part 132 executes first computing processing of generating, height data corresponding to the first direction. Concurrently with the first computing processing, the imaging processing part 131 executes second imaging processing of causing the second illuminating part to emit structured light from a second direction to the measurement target, causing the imaging part 120 to generate image data, and causing a buffer memory 134 to store therein the generated image data. The computing processing part 132 executes second computing processing of generating height data corresponding to the second direction.

A topographic measurement method includes provision of a sample including first surface provided with plurality of salient patterns. The first surface of the sample is illuminated by means of structured light that defines several repetitive patterns. The structured light is emitted at first angle with respect to first surface. A first image of first surface of sample illuminated by structured light is acquired. The first image is acquired at second angle with respect to first surface. A second image of illuminated sample is acquired. The second image differs from first image by value of exposure time. The first image is compared with second image to determine presence of at least one artefact on the first image. A reference image is formed from the first image and the second image. The reference image is devoid of any artefact. A quantity representative of the first surface is calculated from the reference image.

Provided is a method for measuring surface characteristics of at least a portion of an object, including providing a light source; generating a first interference pattern on the at least a portion of the object; capturing an image of the first interference pattern; shifting the phase of the light source to generate a second interference pattern; capturing an image of the second interference pattern; filtering distortion from the interference patterns; extracting a wrapped phase of the at least a portion of the object based on the images; unwrapping the wrapped phase of the at least a portion of the object to generate an unwrapped phase; identifying a computed depth map distance to the at least a portion of the object; and fitting an ideal part to the computed depth map of the at least a portion of the object to measure the surface characteristics.

The present disclosure proposes a three-dimensional computational imaging method and apparatus based on a single-pixel sensor, and a storage medium. The method includes the following. A stripe coding is combined with a two-dimensional imaging coding through a preset optical coding to generate a new optical coding, and the new optical coding is loaded into an spatial light modulator (SLM); a two-dimensional spatial information and depth information of a scene are coupled into a one-dimensional measurement value by using a single-pixel detector and the SLM loaded with the new optical coding; and the two-dimensional spatial information and the depth information of the scene are reconstructed, from the one-dimensional measurement value through a decoupling algorithm, for three-dimensional imaging.