Data-driving indoor scene coloring method
An indoor scene, data-driven technology, applied in image data processing, 3D image processing, instruments, etc., can solve cumbersome, time-consuming, troublesome and other problems
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Embodiment 1
[0062] The flow chart of this method is as follows figure 1 As shown in the figure, it is divided into six major processes: the first is to establish an image-model database and a texture database; then the model classifier is trained on the 3D model of each type of furniture in the image-model database; then the extraction of the furniture in the image-model database is performed. Color theme and establish a probability model; then the optimal coloring scheme is solved according to the established probability model and the color theme input by the user; then each 3D model in the input scene is segmented using the corresponding classifier; The resulting shading scheme assigns a corresponding material to each piece of furniture in the input scene.
[0063] Specifically, as figure 1 As shown, the present invention discloses a data-driven indoor scene coloring method, which mainly includes the following steps: Step 1, establish a database: collect images of different scenes, f...
Embodiment 2
[0107] The implementation hardware environment of this embodiment is: Intel Core i5-45903.3GHz, 8G memory, and the software environment is MicrosoftVisualStudio2010, MicrosoftWindows7Professional, and 3dsmax2012. The input model comes from the network.
[0108] The invention discloses a data-driven indoor scene coloring method, the core of which is to solve the optimal coloring scheme for each furniture in the scene, and divide the furniture grid according to the image, so as to assign corresponding materials to each part, Include the following steps:
[0109] Step 1, database establishment: collect images, models and material samples from the network, and process the collected data to establish an image-model database and a texture database;
[0110] Step 2, training the model classifier: perform feature extraction on the 3D model of each type of furniture in the image-model database and train the classifier;
[0111] Step 3, establish a probability model of image furniture...
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