15 results about "Chamfer distance" patented technology

The invention provides a diffusion-driven channel adaptive point cloud semantic communication method, which belongs to the technical field of wireless communication, and comprises the following steps: obtaining a training point cloud data set, constructing a point cloud feature extraction network based on a hypergraph convolutional neural network as a semantic encoder, and constructing a channel adaptive enhancement module as a channel decoder, a channel adaptive recovery module is constructed at a receiving end as a channel decoder, a diffusion reconstruction network is constructed as a semantic decoder, end-to-end joint training is performed on the point cloud feature extraction network, the channel adaptive enhancement module, the channel adaptive recovery module and the diffusion reconstruction network, the diffusion reconstruction network predicts original point cloud distribution, and the point cloud feature extraction network and the channel adaptive enhancement module are subjected to end-to-end joint training. And a loss function is constructed based on the chamfering distance between the predicted point cloud distribution and the original point cloud. In the communication process, the reconstructed semantic features are input into the diffusion reconstruction network to reconstruct an original point cloud structure. The high-order semantic features of the point cloud can be effectively extracted so as to improve the adaptive capacity of the method to the incomplete point cloud.

The application discloses a sheep three-dimensional point cloud registration method and system based on a local overlapping area. The application estimates the proportion relationship between the overlapping area and the whole point cloud of the target detection body covered by the vertical camera and the horizontal camera by uniformly down-sampling the filtered point cloud data, introduces the judgment of the chamfer distance in the CPD algorithm, obtains the transformation matrix of the local overlapping area, applies the local transformation matrix to the corresponding rotation matrix and translation vector changes of the left and right complete point clouds, and finally, the changed left view, right view and top view point clouds are merged to obtain the registered three-dimensional sheep point cloud. The application effectively solves the problem that the initialization position is sensitive and the asymmetric geometric shape is trapped in a local optimal solution, thereby causing the registration failure. Moreover, the registration speed is improved by 3 times compared with the CPD algorithm before the improvement because the chamfer distance is introduced.

The invention discloses a Kerr black hole equatorial plane particle trajectory rapid prediction method and system. The method comprises the steps of S1, data construction and screening; s2, carrying out sequence standardization and pretreatment; s3, constructing a trajectory prediction model; s4, training a mixed physical constraint loss function; and S5, performing rapid reasoning and trajectory reconstruction. According to the method, complex Kerr space-time three-dimensional motion is simplified into two-dimensional motion of the equatorial plane, end-to-end mapping from an initial condition to a complete trajectory is realized by using deep learning, a time-consuming process of solving a geodesic equation is replaced by single forward propagation of a neural network, and the calculation efficiency is far higher than that of a traditional numerical integration method. According to the method, the robustness of the geometrical morphology of the track is ensured through the chamfering distance loss, and geometrical characteristics such as recent point precession angles can be accurately predicted; the common noise jitter of the neural network is eliminated by the smoothness loss, and the physical rationality of the trajectory is ensured.

According to the intelligent non-convex asteroid shape inversion method based on the optical variation curve and constrained by physical information, physical prior information is fused through a deep learning network (PINAS-Net), and rapid and accurate non-convex asteroid three-dimensional shape inversion is achieved. Specifically, light variation curve data and a scattering parameter c serve as input, features of all curves are extracted through a light variation curve feature extraction module (LCFEM), interaction features among multiple curves are captured through a Transform encoder, and 1024-dimensional global features are obtained; meanwhile, expanding a scalar scattering parameter c into 16-dimensional features, fusing the 16-dimensional features with global features, and importing the 16-dimensional features into the network as physical constraints; and rough point prediction is carried out based on the fused features, and a fine point cloud shape is obtained through a Transform decoder and three-layer up-sampling. The method provided by the invention is verified on observation data and simulation data of a plurality of asteroids (433 Eros, 9 Metis and 21 Luteria), and a reconstruction result is highly consistent with a reference model. Assessment indexes show that the intersection-to-union ratio (IoU) of the method for the non-convex region can reach 0.81, the Chamfer distance is 0.046, and compared with a traditional KTM method, the inversion speed and precision are remarkably improved. The method provides a new technical means for asteroid shape inversion and physical parameter estimation, and has an important planet defense value.

The invention discloses a tactile point cloud completion method based on a dexterous hand topological structure and multi-level feature coding, and belongs to the technical field of robot control. The objective of the invention is to improve the operation capability and perception precision of a robot in a non-visual scene. The method comprises the steps that a mechanical arm carries a dexterous hand to move to a target position, the dexterous hand is gradually closed until an electronic skin sensor records a contact signal, and tactile point cloud data of a target is collected; constructing a tactile point cloud completion model based on a dexterous hand topological structure and multi-level feature coding; a hierarchical loss function of a tactile point cloud completion model based on a dexterous hand topological structure and multi-level feature coding is designed, the hierarchical loss function is composed of coarse point cloud loss and dense point cloud loss, and a chamfering distance CD is adopted as a geometric similarity index for core measurement to measure the spatial deviation between a predicted point cloud and a real point cloud. According to the method, high-quality complementation of a complex object is realized, so that the operation capability and the sensing precision of the robot in a non-visual scene are improved.

The invention discloses an infrared image three-dimensional reconstruction method and system based on a neural radiation field, and belongs to the technical field of computer vision and three-dimensional reconstruction. According to the method, the image quality is improved through dual-stage enhancement preprocessing, accurate estimation of the camera pose is realized by combining SIFT feature extraction and a beam adjustment method, a local-global joint infrared radiation field model is constructed, and a light adaptive SDF optimization strategy is introduced to balance the radiation field and symbol distance function convergence process. According to the method, an infrared imaging process is simulated through cumulative transmissivity modulation, and total loss function optimization model parameters including control loss and structure loss are designed. Experiments show that the infrared scene three-dimensional geometric reconstruction precision and the new view synthesis quality can be remarkably improved, the average chamfering distance is reduced by 87.31%, the peak signal-to-noise ratio is improved by 55.03%, and an efficient solution is provided for infrared three-dimensional reconstruction in complex scenes such as low visibility.

The application discloses a bridge damage monitoring system and method based on image processing, which extracts the structural contour of a two-dimensional image through a deep convolutional neural network, and compares it with the rendered edge generated by perspective projection of a preset three-dimensional information model, and uses the chamfer distance to construct a closed-loop feedback mechanism to iteratively optimize the camera pose, so as to eliminate the pose projection drift caused by GPS rejection or weak texture in a complex environment, thereby accurately mapping the two-dimensional microscopic damage to a three-dimensional macroscopic coordinate system. In this way, the reliability of damage physical size quantification and global positioning is significantly improved, the problems of damage monitoring positioning difficulty and low mapping accuracy in complex inspection conditions are effectively solved, and efficient seamless integration of detection data and digital modeling is realized.

The invention discloses a mechanical dog quality distribution analysis method based on machine vision. The method comprises the following steps: obtaining complete mechanical dog three-dimensional digital model data; according to the material attributes and the geometric volume parameters, generating a mechanical dog overall mass mapping matrix; an optimized depth map is generated by using the improved ZoeDepth model; back-projecting the optimized depth map into dense robotic dog three-dimensional point cloud data; forming a mechanical dog coarse registration result; an IPC algorithm and a Chamfer Distance algorithm are adopted, and a final error measurement result is obtained; obtaining an updated mechanical dog three-dimensional digital model; forming a three-dimensional mass distribution data set; according to the method, a mechanical dog mass distribution thermodynamic diagram, a mechanical dog mass center scatter distribution diagram and a mechanical dog mass center vector diagram are generated, visual display and data storage are carried out, nondestructive testing and high-precision modeling analysis of the internal mass distribution of the mechanical dog are achieved, and the method has the advantages that an entity sensor is not needed, the modeling precision is high, and the visual effect is achieved.

The application discloses a kind of based on similarity gradient guide three-dimensional molecular point cloud generation method and related device, belong to drug molecule design technical field;The method extracts the simplified point cloud representing the space profile of target molecule from its three-dimensional structure as sketching condition information;The sketching condition information is input into the pre-trained three-dimensional molecular point cloud diffusion model;Three-dimensional molecular point cloud diffusion model includes forward noise adding process and reverse denoising process;In the reverse denoising process of three-dimensional molecular point cloud diffusion model, introduce point cloud similarity calculation similarity gradient, to guide sampling process to generate the molecular point cloud matched with the sketching condition information on three-dimensional space configuration.The present application extracts simplified and key sketching information as condition, and calculates the condition gradient using differentiable point cloud similarity measure (such as soft chamfer distance and Gaussian volume overlap degree), realizes the accurate guidance to diffusion process, to generate the sketching structure with similar profile to target molecule.

This invention discloses a method and system for rapid prediction of particle trajectories on the equatorial plane of a Kerr black hole, including steps S1, data construction and screening; step S2, sequence standardization and preprocessing; step S3, trajectory prediction model construction; step S4, training of a hybrid physical constraint loss function; and step S5, rapid inference and trajectory reconstruction. This invention simplifies the complex three-dimensional motion of Kerr spacetime to two-dimensional motion on the equatorial plane, utilizing deep learning to achieve an end-to-end mapping from initial conditions to the complete trajectory. It replaces the time-consuming process of solving geodesic equations with a single forward propagation of the neural network, achieving computational efficiency far exceeding traditional numerical integration methods. This invention ensures the robustness of trajectory geometry through chamfer distance loss, accurately predicting geometric features such as the perihelion precession angle; smoothness loss eliminates common noise jitter in neural networks, ensuring the physical rationality of the trajectory.

This invention proposes a remote sensing rotating target detection algorithm based on dynamic orientation-aware attention. First, addressing the problem of insufficient multi-scale feature fusion, a cross-scale dense fusion feature pyramid network is proposed. Through full-scale dense connections and feature distillation mechanisms, the deep fusion of semantic and detailed information is enhanced. Second, addressing the problem of insufficient target orientation awareness, a dynamic orientation-aware attention module is proposed. This module dynamically generates rotating convolutional kernels guided by angle information, achieving alignment between the receptive field and the target orientation. Finally, to address the training instability caused by angle periodicity, a Gaussian weighted chamfered distance loss is constructed, transforming angle error into a geometric distance penalty. An angle-aware Gaussian weight is introduced to alleviate angle discontinuities. This invention effectively solves the problems of insufficient multi-scale feature fusion, insufficient orientation awareness, and training instability caused by angle periodicity in remote sensing rotating target detection.

The invention discloses an engineering component section identification method based on rotation optimization and Chamfer distance, belongs to the field of building information models and intelligent construction, and can solve the problems that semantic information of a component is lost and the section type is difficult to identify after a BIM / CAD model is converted into a general three-dimensional grid format. And a high-fidelity geometric model with standard section attributes is provided for a simulation pre-processing engine. The method does not depend on parameterization information of an original model, is only based on three-dimensional grid geometric data, and realizes high-precision and high-robustness identification and standardized matching of typical component section types such as beams, columns, supports and the like through extension direction analysis and based on continuous optimization matching of differential transformation and Chamfer distance.

The application discloses a point cloud completion method based on a multi-head attention mechanism feature encoding and double-discriminator decoding, wherein, for a local feature perception encoder module, a multi-head attention mechanism and a multi-layer perception machine are combined to perform feature encoding on an existing point cloud, similar point features of an input point cloud are adaptively aggregated, and the perception of local features in a feature extraction process is improved; a double-branch decoder controlled by a discriminator sets the discriminator in a local feature and global feature decoding process, can discriminate the generated skeleton point cloud and fine point cloud based on the input point cloud features, and ensures that the generated point cloud conforms to the global features and detailed features of the existing point cloud. Through the cooperation of the above two modules, the point cloud shape completion effect can be effectively enhanced, the chamfer distance loss of the generated point cloud is reduced, and the fidelity of the generated point cloud is improved.

The application discloses a feature-driven point cloud completion method and system for a CAD parameterized model, relates to the technical field of three-dimensional geometry processing and computer vision, and comprises the following steps: constructing a multi-source feature line collection and standardization system, obtaining feature line candidates through multiple paths such as analysis of CAD information, mesh point cloud geometric clues and interactive editing rules, and outputting a unified format feature representation after consistency evaluation, cleaning and repairing; designing a local feature extraction method based on adaptive weights of multiple relationships, dynamically adjusting weights according to the relationship between target points and neighbor points in three-dimensional space and high-dimensional feature space in a DGCNN framework, and quantifying the contribution of each neighbor point to the local feature of the target point; and proposing a feature line deep information injection technology, fusing feature line geometric information into a point cloud completion deep learning model through an encoder, using a feature line weighted chamfer distance loss function to strengthen the fidelity of boundary and acute angle regions, and effectively improving the accuracy and geometric detail retention capability of point cloud completion.

The invention relates to a vehicle type identification method based on a three-dimensional point cloud, and a robot operation method and system based on a vehicle type. The method comprises the following steps: collecting an ROI point cloud of a to-be-identified vehicle; determining a registration score and a chamfering distance between the point cloud of the ROI and the point cloud of each prior vehicle model template; according to each registration score and the chamfering distance, determining a matching score between the ROI point cloud and each prior vehicle model template point cloud; and sorting the priori vehicle model template point clouds according to the matching scores, and taking the vehicle model of the priori vehicle model template point cloud corresponding to the highest matching score as the vehicle model of the to-be-identified vehicle. The problems of low identification precision, poor robustness, low efficiency, high cost, weak system adaptability and the like in the automatic identification process of the vehicle type in an industrial scene are solved.