Beihang University

Sijia Wen

Bin Zhou

Qiang Fu

Qinping Zhao

Xiaowu Chen

Feng Lu

Xiaotian Wang

The embodiments of the present disclosure disclose image defogging method. A specific mode of carrying out the method includes the steps to: obtain a polarized fog-free scene image set, generate a first quintuple based on each polarized fog-free scene image, and obtain a first quintuple set; extract pixel coordinates meeting a preset condition from the significant polarization image included in each first quintuple, to obtain a pixel coordinate group to generate a second quintuple; generate a simulated polarization foggy scene image sequence based on each second quintuple, to obtain a simulated polarization foggy scene data set; design a polarization state attention neural network; obtain a polarized foggy scene image, and input the polarized foggy scene image into the polarization state attention neural network to obtain a polarized defogging image. This embodiment can still improve the accuracy of image defogging with poor lighting conditions.

Image defogging method based on simulated polarization foggy scene data set

The embodiments of the present disclosure disclose method for calculating image highlight reflection separation. A specific implementation of the method includes: controlling a polarized color sensor to shoot a polarized color image, and calculating a polarized chromaticity image; clustering the pixels of diffuse reflection components whose chromaticity difference is within a preset range, to obtain a pixel sequence set; using a robust principal component analysis algorithm for each pixel, to obtain an initial separation result; further processing the polarized color image for highlight reflection separation, to obtain a highlight reflection separation result, and generating a diffuse reflection image. This implementation can achieve highlight reflection separation for images without being affected by the lighting information, and obtain a highlight reflection separation result with higher accuracy through the color and polarization information.

Method for calculating image highlight reflection separation based on polarization guidance

Disclosed are a method and apparatus for adaptively constructing a three-dimensional indoor scenario, the method including: establishing an object association map corresponding to different scenario categories according to an annotated indoor layout; selecting a corresponding target indoor object according to room information inputted by a user and the object association map; generating a target indoor layout according to preset room parameters inputted by the user and the annotated indoor layout; and constructing a three-dimensional indoor scenario according to the target indoor object and the target indoor layout. The disclosed method and apparatus help improving the efficiency in constructing the three-dimensional scenario.

Method and apparatus for adaptively constructing three-dimensional indoor scenario

The disclosed provides a posture-guided cross-category method and device for combination modeling of cross-category 3D models, the method including: receiving a first posture model inputted by a user; calculating similarities between q first regions of the first posture model and q second regions of each of second posture models in a preset model database, respectively; where the model database includes a plurality of models partitioned into model components, and second posture models corresponding to the models; where the q first regions correspond to the q second regions one on one, where q is an integer greater than or equal to 2; selecting a corresponding plurality of model components of the q second regions of the second posture model according to the similarities; and combining the selected plurality of model components to generate a 3D model. The embodiment of the present application can combine cross-category models with large structural differences.

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Beijing, China

Sijia Wen