Abstract: In various embodiments, a differentiable rendering application enables an inverse rendering application to infer attributes associated with a 3D scene. In operation, the differentiable rendering application renders an image based on a first set of points associated with the 3D scene. The differentiable rendering application then generates an artificial gradient that approximates a change in a value of a first pixel included in the image with respect to a change in an attribute of a first point included in the first set of points. Subsequently, the inverse rendering application performs optimization operation(s) on the first point based on the artificial gradient to generate a second set of points. Notably, an error associated with the second set of points is less than an error associated with the first set of points.

Abstract: In various embodiments, a differentiable rendering application enables an inverse rendering application to infer attributes associated with a 3D scene. In operation, the differentiable rendering application renders an image based on a first set of points associated with the 3D scene. The differentiable rendering application then generates an artificial gradient that approximates a change in a value of a first pixel included in the image with respect to a change in an attribute of a first point included in the first set of points. Subsequently, the inverse rendering application performs optimization operation(s) on the first point based on the artificial gradient to generate a second set of points. Notably, an error associated with the second set of points is less than an error associated with the first set of points.

Abstract: The present disclosure provides a three-dimensional point cloud model reconstruction method and a device.

Abstract: A method for reconstructing a three-dimensional model of point clouds includes following steps: a, scanning to obtain point clouds of an object required for a three-dimensional model reconstruction; b, analyzing quality of the obtained point clouds; c, computing a new scanning view based on the analyzed point clouds; d, scanning according to the new scanning view and updating the point clouds of step a based on point clouds obtained by the scanning according to the new scanning view in real time; and e, reconstructing a three-dimensional model according to the point clouds updated in real time. The invention further relates to a system for reconstructing a three-dimensional model of point clouds. The invention can realize full automatic reconstruction of a three-dimensional model and create a model of point clouds with high quality. In addition, the invention is easy to implement and can achieve high efficiency.

Abstract: The present disclosure provides a three-dimensional point cloud model reconstruction method, a computer readable storage medium and a device.

Abstract: A method for reconstructing a three-dimensional model of point clouds includes following steps: a, scanning to obtain point clouds of an object required for a three-dimensional modelreconstruction; b, analyzing quality of the obtained point clouds; c, computing a new scanning view based on the analyzed point clouds; d, scanning according to the new scanning view and updating the point clouds of step a based on point clouds obtained by the scanning according to the new scanning view in real time; and e, reconstructing a three-dimensional model according to the point clouds updated in real time. The invention further relates to a system for reconstructing a three-dimensional model of point clouds. The invention can realize full automatic reconstruction of a three-dimensional model and create a model of point clouds with high quality. In addition, the invention is easy to implement and can achieve high efficiency.

Abstract: A method for extracting a skeleton form a point cloud includes: obtaining inputted point cloud sampling data; contracting the point cloud using an iterative formula and obtaining skeleton branches, the iterative formula is: argmin X ? ? i ? I ? ? ? j ? J ? ? ? x i - q i ? ? ? ? ( ? x j - q j ? ) + R ? ( X ) , ? wherein R ? ( X ) = ? i ? I ? ? ? i ? ? i ? ? I ? \ ? { i } ? ? ? ? ( ? x i - x i ? ? ) ? i ? ? x i - x i ? ? , ? ? ( r ) = ? - 4 ? ? r 2 h 2 , wherein J represents a point set of the point cloud sampling data, q represents the sampling points in the point set J, I represents a neighborhood point set of the sampling points q, x represents the neighborhood points in the neighborhood point set I, R is a regular term, ? is a weighting coefficient, h is a neighborhood radius of the neighborhood point set I, and ? is a distribution coefficient; and connecti

Abstract: A method for extracting a skeleton form a point cloud includes: obtaining inputted point cloud sampling data; contracting the point cloud using an iterative formula and obtaining skeleton branches, the iterative formula is: arg ? ? min X ? ? i ? I = ? ? j ? J ? ? x i - q i ? ? ? ? ( ? x j - q j ? ) + R ? ( X ) , wherein R ? ( X ) = ? i ? I ? ? i ? ? i ? ? I / { i } ? ? ? ( ? x i - x i ? ? ) ? i ? ? x i - x i ? ? , ? ? ( r ) = ? 4 ? ? r 2 h 2 , wherein J represents a point set of the point cloud sampling data, q represents the sampling points in the point set J, I represents a neighborhood point set of the sampling points q, x represents the neighborhood points in the neighborhood point set I.