Abstract: A method for generating a hexahedral mesh based on a closed-form polycube includes steps of: converting a surface triangular mesh into a tetrahedral mesh; obtaining an initial 3-dimensional frame field; according surface normal constraint, generating a smooth frame field without singular line inside the model; extracting a transition relation on the cut surface based on the smooth frame field; providing Poisson optimization on cut model based on the frame field for obtaining a preliminary parameterized result; using L1-optimization for forming a final closed-form polycube; optimizing with an mixed integer for obtaining a final parameterized result; and extracting the hexahedral mesh. The method automatically generates the hexahedral mesh from the triangular mesh, and has nothing to do with an initial position of an object, which is able to better satisfy a characteristic constraint of the model; and for a model with complex topology, high quality hexahedral mesh is available.

Abstract: The invention discloses a real-time rendering method based on energy consumption-error precomputation, comprising: determining the spatial level structure of the scene to be rendered through adaptive subdivision of the space positions and look space of the camera browsable to the user in the 3D scene to be rendered; during the process of adaptive subdivision of the space, for each position subspace obtained at the completion of each subdivision, obtaining the error and energy consumption of the camera for rendering the 3D scene using a plurality of sets of preset rendering parameters in each look subspace at each vertex of the bounding volume that bounds the position subspace, and Pareto curve of the corresponding vertex and look subspace is built based on the error and energy consumption; based on the current camera viewpoint information, searching and obtaining the target Pareto curve in the spatial level structure to determine a set of rendering parameters satisfying the precomputation condition as optimum

Abstract: A method for generating a hexahedral mesh based on a closed-form polycube includes steps of: converting a surface triangular mesh into a tetrahedral mesh; obtaining an initial 3-dimensional frame field; according surface normal constraint, generating a smooth frame field without singular line inside the model; extracting a transition relation on the cut surface based on the smooth frame field; providing Poisson optimization on cut model based on the frame field for obtaining a preliminary parameterized result; using L1-optimization for forming a final closed-form polycube; optimizing with an mixed integer for obtaining a final parameterized result; and extracting the hexahedral mesh. The method automatically generates the hexahedral mesh from the triangular mesh, and has nothing to do with an initial position of an object, which is able to better satisfy a characteristic constraint of the model; and for a model with complex topology, high quality hexahedral mesh is available.

Abstract: Embodiments of the present disclosure disclose a static object reconstruction method and system that are applied to the field of graph and image processing technologies. In the embodiments of the present disclosure, when a static object reconstruction system does not obtain, by means of calculation, an extrinsic camera parameter in a preset time when calculating the extrinsic camera parameter based on a three-dimensional feature point, it indicates that depth data collected by a depth camera is lost or damaged, and a two-dimensional feature point is used to calculate the extrinsic camera parameter to implement alignment of point clouds of a frame of image according to the extrinsic camera parameter. In this way, a two-dimensional feature point and a three-dimensional feature point are mixed, which can implement that a static object can also be successfully reconstructed when depth data collected by a depth camera is lost or damaged.

Abstract: A camera tracking method includes obtaining an image set of a current frame; separately extracting feature points of each image in the image set of the current frame; obtaining a matching feature point set of the image set according to a rule that scene depths of adjacent regions on an image are close to each other; separately estimating, a three-dimensional location of a scene point corresponding to each pair of matching feature points in a local coordinate system of the current frame and a three-dimensional location of the scene point in a local coordinate system of a next frame; estimating a motion parameter of the binocular camera on the next frame using invariance of center-of-mass coordinates to rigid transformation according to the three-dimensional location of the scene point corresponding to the matching feature points; and optimizing the motion parameter of the binocular camera on the next frame.

Abstract: Embodiments of the present disclosure disclose a static object reconstruction method and system that are applied to the field of graph and image processing technologies. In the embodiments of the present disclosure, when a static object reconstruction system does not obtain, by means of calculation, an extrinsic camera parameter in a preset time when calculating the extrinsic camera parameter based on a three-dimensional feature point, it indicates that depth data collected by a depth camera is lost or damaged, and a two-dimensional feature point is used to calculate the extrinsic camera parameter to implement alignment of point clouds of a frame of image according to the extrinsic camera parameter. In this way, a two-dimensional feature point and a three-dimensional feature point are mixed, which can implement that a static object can also be successfully reconstructed when depth data collected by a depth camera is lost or damaged.

Abstract: A camera pose tracking apparatus may track a camera pose based on frames photographed using at least three cameras, may extract and track at least one first feature in multiple-frames, and may track a pose of each camera in each of the multiple-frames based on first features. When the first features are tracked in the multiple-frames, the camera pose tracking apparatus may track each camera pose in each of at least one single-frame based on at least one second feature of each of the at least one single-frame. Each of the at least one second feature may correspond to one of the at least one first feature, and each of the at least one single-frame may be a previous frame of an initial frame of which the number of tracked second features is less than a threshold, among frames consecutive to multiple-frames.

Abstract: An image processing apparatus and method may estimate binocular disparity maps of middle views from among a plurality of views through use of images of the plurality of views. The image processing apparatus may detect a moving object from the middle views based on the binocular disparity maps of the frames. Pixels in the middle views may be separated into dynamic pixels and static pixels through detection of the moving object. The image processing apparatus may apply bundle optimization and a local three-dimensional (3D) line model-based temporal optimization to the middle views so as to enhance binocular disparity values of the static pixels and dynamic pixels.

Abstract: A camera pose tracking apparatus may track a camera pose based on frames photographed using at least three cameras, may extract and track at least one first feature in multiple-frames, and may track a pose of each camera in each of the multiple-frames based on first features. When the first features are tracked in the multiple-frames, the camera pose tracking apparatus may track each camera pose in each of at least one single-frame based on at least one second feature of each of the at least one single-frame. Each of the at least one second feature may correspond to one of the at least one first feature, and each of the at least one single-frame may be a previous frame of an initial frame of which the number of tracked second features is less than a threshold, among frames consecutive to multiple-frames.

Abstract: An image processing apparatus and method may estimate binocular disparity maps of middle views from among a plurality of views through use of images of the plurality of views. The image processing apparatus may detect a moving object from the middle views based on the binocular disparity maps of the frames. Pixels in the middle views may be separated into dynamic pixels and static pixels through detection of the moving object. The image processing apparatus may apply bundle optimization and a local three-dimensional (3D) line model-based temporal optimization to the middle views so as to enhance binocular disparity values of the static pixels and dynamic pixels.