Abstract: A scene scanning method and a scene scanning system, adapted to obtain information for reconstructing a 3D model, are provided. The method comprises projecting, by a characteristic projecting unit, a characteristic pattern by applying an invisible light in a scene; capturing, by a sensing unit, a data of the scene and the characteristic pattern; and receiving, by a processing unit, the data of the scene and the characteristic pattern captured by the sensing unit.

Abstract: In an embodiment of the disclosure, a handheld 3D scanning device is provided. The handheld 3D scanning device comprises at least one first 3D sensing module, at least one second 3D sensing module, and a fixing unit. Each of the at least one first 3D sensing module and the at least one second 3D sensing module comprises at least one projecting unit and at least one image sensing unit for performing a 3D scanning to an object to be measured. The fixing unit is provided to fix the at least one first 3D sensing module and the at least one second 3D sensing module at specific locations, respectively.

Abstract: A scene scanning method and a scene scanning system, adapted to obtain information for reconstructing a 3D model, are provided. The method comprises projecting, by a characteristic projecting unit, a characteristic pattern by applying an invisible light in a scene; capturing, by a sensing unit, a data of the scene and the characteristic pattern; and receiving, by a processing unit, the data of the scene and the characteristic pattern captured by the sensing unit.

Abstract: An object scanning method comprising following steps is provided. An object is scanned and a depth information of the object is captured by a depth sensor. A motor is moved and another depth information of the object after the movement of the motor is captured at least once. Under the circumstance that the axis coordinate of the motor are not calibrated, a movement amount of the motor is captured. A comparison of at least one feature point is made between two depth information of the object according to the movement amount of the motor, and an iterative algorithm is used to obtain corresponding coordinate of each feature point until the comparison of each feature point is completed. A 3D model of the object is created according to the corresponding coordinate of each feature point.

Abstract: A device for acquiring depth image, a calibrating method and a measuring method therefore are provided. The device includes at least one projecting device, at least one image sensing device, a mechanism device and a processing unit. The projecting device projects a projection pattern to a measured object. The image sensing device is controlled to adjust a focal length and focus position, and therefore sense real images. The mechanism device adjusts a location and/or a convergence angle of the image sensing device. The processing unit calibrates the at least one image sensing device and generates a three dimension (3D) measuring parameter set at a model focal length according to a plurality of image setting parameter reference sets corresponding to a model focal length and a plurality of default node distances, respectively, and then estimates a depth map or depth information of the measured object.

Abstract: A three dimensional (3D) sensing method and an apparatus thereof are provided. The 3D sensing method includes the following steps. A resolution scaling process is performed on a first pending image and a second pending image so as to produce a first scaled image and a second scaled image. A full-scene 3D measurement is performed on the first and second scaled images so as to obtain a full-scene depth image. The full-scene depth image is analyzed to set a first region of interest (ROI) and a second ROI. A first ROI image and a second ROI image is obtained according to the first and second ROI. Then, a partial-scene 3D measurement is performed on the first and second ROI images accordingly, such that a partial-scene depth image is produced.

Abstract: A depth image acquiring device is provided, which includes at least one projecting device and at least one image sensing device. The projecting device projects a projection pattern to an object. The image sensing device senses a real image. In addition, the projecting device also serves as a virtual image sensing device. The depth image acquiring device generates a disparity image by matching three sets of dual-images formed by two real images and one virtual image, and generates a depth image according to the disparity image. In addition, the depth image acquiring device also generates a depth image by matching two real images, or a virtual image and a real image without verification.

Abstract: A device for acquiring depth image, a calibrating method and a measuring method therefore are provided. The device includes at least one projecting device, at least one image sensing device, a mechanism device and a processing unit. The projecting device projects a projection pattern to a measured object. The image sensing device is controlled to adjust a focal length and focus position, and therefore sense real images. The mechanism device adjusts a location and/or a convergence angle of the image sensing device. The processing unit calibrates the at least one image sensing device and generates a three dimension (3D) measuring parameter set at a model focal length according to a plurality of image setting parameter reference sets corresponding to a model focal length and a plurality of default node distances, respectively, and then estimates a depth map or depth information of the measured object.

Abstract: A three dimensional (3D) sensing method and an apparatus thereof are provided. The 3D sensing method includes the following steps. A resolution scaling process is performed on a first pending image and a second pending image so as to produce a first scaled image and a second scaled image. A full-scene 3D measurement is performed on the first and second scaled images so as to obtain a full-scene depth image. The full-scene depth image is analyzed to set a first region of interest (ROI) and a second ROI. A first ROI image and a second ROI image is obtained according to the first and second ROI. Then, a partial-scene 3D measurement is performed on the first and second ROI images accordingly, such that a partial-scene depth image is produced.

Abstract: A method and apparatus for tracking a target object are provided. A plurality of images is received, and one of the images is selected as a current image. A specific color of the current image is extracted. And the current image is compared with a template image to search a target object in the current image. If the target object is not found in the current image, a previous image with the target object is searched in the images received before the current image. And the target object is searched in the current image according to an object feature of the previous image. The object feature and an object location are updated into a storage unit when the target object is found.

Abstract: A depth image acquiring device is provided, which includes at least one projecting device and at least one image sensing device. The projecting device projects a projection pattern to an object. The image sensing device senses a real image. In addition, the projecting device also serves as a virtual image sensing device. The depth image acquiring device generates a disparity image by matching three sets of dual-images formed by two real images and one virtual image, and generates a depth image according to the disparity image. In addition, the depth image acquiring device also generates a depth image by matching two real images, or a virtual image and a real image without verification.

Abstract: A gesture recognition system includes an image pick-up device, a processor, an operation engine, an optimal template selection means, and a display terminal. The image pick-up device is for capturing an image containing a natural gesture. The processor is for finding out a skin edge of a skin part from the image, and then classifying the skin edge into multiple edge parts at different angles. The operation engine has multiple parallel operation units and multiple gesture template libraries of different angle classes. These parallel operation units respectively find out gesture templates most resembling the edge parts in the gesture template libraries of different angle classes. The optimal template selection means selects an optimal gesture template from the resembling gesture templates found out by the parallel operation units. The display terminal is for displaying an image of the optimal gesture template. Thereby, marker-less and real-time gesture recognition is achieved.

Abstract: A method and apparatus for tracking a target object are provided. A plurality of images is received, and one of the images is selected as a current image. A specific color of the current image is extracted. And the current image is compared with a template image to search a target object in the current image. If the target object is not found in the current image, a previous image with the target object is searched in the images received before the current image. And the target object is searched in the current image according to an object feature of the previous image. The object feature and an object location are updated into a storage unit when the target object is found.

Abstract: A collision simulation method of a three dimensional (3D) object is provided, wherein the 3D object is composed of a plurality of polygonal meshes. First, a collision between the polygonal meshes and an object is detected. When one of the polygonal meshes is collided by the object, at least one virtual vertex is generated at a first position where the polygonal mesh is collided by the object, wherein the polygonal mesh includes a plurality of vertexes. Then, the virtual vertex is connected to the vertexes to form a plurality of sub meshes. Next, a force between the object and the virtual vertex is calculated to update the first position of the virtual vertex into a second position. After that, forces between the virtual vertex and the vertexes are calculated according to the second position of the virtual vertex so as to update the positions of the vertexes.

Abstract: A gesture recognition system includes an image pick-up device, a processor, an operation engine, an optimal template selection means, and a display terminal. The image pick-up device is for capturing an image containing a natural gesture. The processor is for finding out a skin edge of a skin part from the image, and then classifying the skin edge into multiple edge parts at different angles. The operation engine has multiple parallel operation units and multiple gesture template libraries of different angle classes. These parallel operation units respectively find out gesture templates most resembling the edge parts in the gesture template libraries of different angle classes. The optimal template selection means selects an optimal gesture template from the resembling gesture templates found out by the parallel operation units. The display terminal is for displaying an image of the optimal gesture template. Thereby, marker-less and real-time gesture recognition is achieved.