7results about How to "Efficient reconstruction" patented technology

The present application relates to the technical field of computer graphics, computer vision and three-dimensional reconstruction, and particularly relates to an immersive scene reconstruction rendering method and system based on adaptive 3D Gaussian sputtering, which acquires scene initial point cloud data and camera parameters, constructs a 3D Gaussian point cloud model containing multiple attributes and generates sky point cloud; based on the mechanism of pulse neurons, the opacity of Gaussian points is adaptively controlled, and according to the gradient information and geometric attributes of Gaussian points, splitting, copying or pruning operations are performed to optimize the distribution of point cloud; through a deep learning network, the camera pose is corrected, large-scale scenes are divided into blocks for parallel training, and a bilateral grid post-processing is combined with multiple loss functions to optimize rendering, and the sky point cloud is subjected to exclusive constraint, so that the technical defects of the prior art are solved, and the immersive scene application requirements in the fields of VR / AR, digital twin and the like are met.

The application discloses a kind of based on structure-detail separation bidirectional recurrent neural network super-resolution method, belong to video super-resolution technical field.The application mainly includes the following steps:1, the spatial feature, structure feature and detail feature of each frame picture of low-resolution video are extracted;2, the forward and backward optical flow of each frame picture is calculated;3, the spatial feature, structure / detail feature and forward optical flow of each frame picture are input into network, and the forward structure / detail feature of current frame is obtained;4, the spatial feature, structure / detail feature, backward optical flow and forward structure / detail feature of each frame picture are input into network, and the backward structure / detail feature of current frame is obtained;5, the spatial feature, structure / detail feature and forward and backward structure / detail feature of each frame picture are calculated to obtain reconstruction structure / detail feature, and high-resolution result is obtained by calculating the result with spatial feature.The video super-resolution method based on the application can obtain high-quality output with more details.

This invention belongs to the field of blade trajectory monitoring technology, specifically involving a method for dynamic monitoring of the three-dimensional trajectory of wind turbine blades by fusing imagery and laser point cloud data. The steps include: fixing a camera and scanner with a rigid connection device for use as a dynamic monitoring system for the three-dimensional trajectory of the wind turbine blade; calibrating the camera; calibrating the relative position and attitude between the camera and scanner; acquiring image data and laser point cloud data during normal operation of the wind turbine; performing motion compensation correction on the laser point cloud data; calculating the three-dimensional coordinates corresponding to the monitoring point; obtaining a time-series image-side coordinate sequence of the monitoring point; and calculating the three-dimensional coordinate sequence corresponding to the time-series image-side coordinate sequence of the monitoring point, i.e., the three-dimensional motion trajectory of the monitoring point on the wind turbine blade during operation. This invention uses imagery and laser point cloud technology to monitor the three-dimensional trajectory of wind turbine blades, requiring no auxiliary markers, eliminating complex calibration, offering flexible deployment and high accuracy, and efficiently supporting blade safety assessment and maintenance decisions.

The application discloses a five-dimensional seismic data interpolation method and system based on coordinates, constructs a point-by-point seismic data interpolation model based on a NeRF theory, modifies an MLP framework by combining an additional convolution layer on the basis of the point-by-point interpolation model, constructs a profile-by-profile seismic data interpolation model, introduces kernel norm regularization to construct an objective function, reads original five-dimensional seismic data D obs and inputs the five-dimensional seismic data with missing traces into the network to realize reconstruction of the five-dimensional seismic data. The application eliminates the need for additional labeled data, utilizes unique characteristics of seismic data, enables a convolution network decoder to output data profile by profile, can significantly improve data processing efficiency by 40 times, and increases kernel norm regularization in the objective function, so that the noise resistance and robustness of the model can be effectively improved.

The invention discloses a point cloud geometric compression method based on deep learning, and the method comprises the following steps: (1) segmenting an initial point cloud according to the shape, so as to ensure that each block corresponds to different parts of an object; (2) applying a k-Means clustering algorithm to the segmented point cloud data to further refine the blocks so as to enable the points between the blocks to be relatively balanced; (3) inputting the subdivided point cloud data into a Point Net + + network for feature extraction so as to capture local geometric features; (4) constructing a graph structure in the processed point cloud data to enhance feature interaction between blocks; (5) fusing the extracted point cloud features by using a graph attention mechanism, and focusing on parts with similar shapes in adjacent blocks; and (6) adding point information into the features, and carrying out arithmetic coding and transmission on the features. And (7) finally, gradually reconstructing each block according to the features, and interpolating the point cloud blocks according to the point information to obtain a final reconstructed point cloud. Through the method, the geometric compression and reconstruction performance of the point cloud is remarkably improved.

The application provides a TDLAS combined detection method, which is based on an existing gas two-dimensional concentration distribution reconstruction method based on TDLAS remote detection, and combines a TDLAS in-situ detection system and a TDLAS remote detection system to detect indoor gas concentration, optimizes the gas detection method, corrects the iteration result by using in-situ detection data in each iteration of the reconstruction process, improves the image reconstruction algorithm, and proposes a gas two-dimensional concentration distribution reconstruction method based on TDLAS combined detection. The application improves the reconstruction accuracy and reduces the reconstruction time.

The application discloses a space-based infrared dim small target detection method based on a vector signal, in order to overcome the low timeliness and large resource consumption bottleneck of the traditional "imaging-caching-preprocessing-detection" link, skips the image caching and preprocessing link, and directly takes the DN value vector signal read out by the detector as input. Wherein, the two-dimensional or higher-dimensional features of the target are recovered from the one-dimensional vector signal through the network based on the CNN and BERT mechanisms and with the help of the Markov random field model; the network based on the self-supervised U-Net structure is used to decouple the target and background clutter features, and the multi-scale detection mechanism is combined to selectively enhance the target features to complete the detection; the network is lightened through the multi-teacher knowledge distillation architecture, and pruning and quantization are combined to obtain a lightened detection model suitable for the resource limited environment on the satellite. The application realizes the "detection and calculation integration", and significantly improves the timeliness and resource efficiency of the space-based infrared dim small target detection.