Visual color matching method for ancient building

Abstract

The invention relates to the technical field of building design, in particular to an ancient building visual color matching method, which comprises the following steps of: 1, acquiring multi-source data; step 2, color feature extraction; 3, constructing a historical color knowledge base; step 4, constructing a color matching model; and step 5, performing three-dimensional visual rendering. According to the historic building visual color matching method, qualitative analysis of'regional culture-natural environment 'is converted into quantitative collection of multispectral images and three-dimensional point clouds, and spatial accurate positioning of color features is achieved; a standardized knowledge base containing historical periods and technologies is constructed, and the fragmentation problem of traditional color matching experience is solved; through physical realistic rendering, the method is expanded to color simulation of the ancient building under different climate conditions, and the recovery credibility is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of architectural design, and in particular relates to a visual color matching method for ancient buildings. BACKGROUND

[0002] In the construction industry, architectural design is the key link of rational utilization of construction materials. In the process of architectural design, scientific and reasonable use of color elements can enhance the social effect and influence of the building, bring better visual effect and pleasure, and meet people's pursuit of beauty and life style. With the acceleration of urbanization, the construction industry is developing rapidly, and color is not only an expression way but also a key to reflect the characteristics of the city. The prior art has the following defects in the color matching of ancient buildings: 1. Lack of multi-dimensional data fusion in traditional color collection means: The existing color matching method for ancient buildings relies on single-point sampling or two-dimensional images, which is difficult to fully capture the spatial color distribution and material characteristics of building components. For example, traditional photography only covers the visible light band, and cannot obtain the pigment composition information in the near-infrared spectrum, resulting in deviation in color feature extraction. At the same time, it lacks the support of three-dimensional point cloud data, and cannot accurately associate the color with the spatial position of the building component, making it difficult to realize fine color matching analysis.

[0003] 2. Fragmentation and lack of standardization of historical color knowledge system: Traditional methods lack the excavation of historical and cultural connotations of ancient building colors, and rely on scattered documents or experience inheritance. No systematic color knowledge base is established. For example, color samples of different historical periods lack standardized conversion in CIE Lab space, resulting in errors in color comparison across regions and eras. In addition, the cultural symbolic meaning of color (such as regional folk customs and craft background) lacks structured annotation, making it difficult to support scientific color matching scheme design.

[0004] 3. Visualization rendering is out of touch with the actual scene and verification mechanism is missing: Existing visualization technologies mostly use simple texture mapping and do not consider the influence of lighting environment on color (such as solar azimuth angle and atmospheric transmittance), resulting in large deviation between rendering effect and actual scene. At the same time, there is a lack of color matching verification process based on archaeological data, such as not comparing the color difference between the generated scheme and the fragment color, which makes it difficult to ensure the accuracy of the restoration. In addition, traditional methods do not support interactive color matching optimization, which cannot meet the personalized design needs.

[0005] In view of the above problems, the present application provides a visual color matching method for ancient buildings. SUMMARY

[0006] The main purpose of the present application is to provide a visual color matching method for ancient buildings, which can effectively solve the problems in the background art.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for visualizing color schemes in ancient architecture includes the following steps: Step 1: Multi-source data acquisition: Collect multispectral images of ancient buildings, 3D point cloud data, historical documents, and regional cultural characteristic data; the multispectral images cover the visible and near-infrared bands with a resolution ≥200 dpi; the 3D point cloud data density ≥100 points / square meter.

[0008] Step 2, Color Feature Extraction: Color information is extracted from the multispectral image using a deep learning model, and the spatial color distribution of building components is obtained by combining it with 3D point cloud data. The deep learning model is an improved U-Net network that includes an attention mechanism module.

[0009] Step 3: Construction of a historical color knowledge base: Integrate historical documents, archaeological data, and traditional craft information to establish a database of ancient building colors that includes different historical periods and regional styles. The database contains at least 1,000 sets of traditional color samples and their cultural background information.

[0010] Step 4: Color Matching Model Construction: Based on the historical color knowledge base, a color matching model is constructed using the support vector machine algorithm. The input of the model is the extracted color features and regional cultural features, and the output is the adapted traditional color matching scheme.

[0011] Step 5, 3D visualization rendering: The color scheme is mapped onto the 3D model of the ancient building, and the color effect under different ambient light conditions is displayed by combining the lighting simulation algorithm. The lighting simulation algorithm takes into account parameters such as solar azimuth angle and atmospheric transmittance.

[0012] Preferably, in the multi-source data acquisition step, the regional cultural characteristic data includes: Data on the symbolic meanings of local traditional colors were extracted from local chronicles and folklore documents using natural language processing technology. Climate and environmental data, including annual average temperature, humidity, and sunshine duration, are used for color weather resistance analysis.

[0013] Preferably, the specific process of color extraction using the improved U-Net network in the color feature extraction step includes: A residual connection module is used to enhance feature extraction capabilities, and skip connections are added between the encoder and decoder; By using spatial attention and channel attention mechanisms, irrelevant color information is suppressed, highlighting the color characteristics of architectural components.

[0014] Preferably, the processing of traditional color samples in the historical color knowledge base construction step includes: The CIE Lab color space is used for standardized conversion, achieving an accuracy of ΔE≤1.5; Each color sample is labeled with its historical period, regional style, application area, and technological information.

[0015] Preferably, in the color matching model construction step, the kernel function of the support vector machine algorithm is a radial basis function, the parameter γ ranges from 0.01 to 10, the penalty parameter C ranges from 1 to 100, and the optimal parameters are determined through cross-validation.

[0016] Preferably, the 3D visualization rendering step employs physically based rendering (PBR) technology, and the technical parameters include: The resolution of the metallic texture and roughness texture should be ≥2048×2048; The sampling rate of the ambient occlusion (AO) map is ≥4×4; Supports real-time global illumination calculation with a frame rate of ≥30fps.

[0017] Preferably, the method further includes a color scheme optimization step: receiving user adjustment instructions through an interactive interface, and optimizing the color scheme using a genetic algorithm, wherein the population size of the genetic algorithm is 50-100, the crossover probability is 0.6-0.8, and the mutation probability is 0.01-0.05.

[0018] Preferably, the interactive interface supports the following operations: The colors of building components are locally adjusted with an accuracy of ±5° for the H component, ±5% for the S component, and ±5% for the V component in the HSV color space. Preview the adjusted 3D rendering effect in real time.

[0019] Preferably, the method also includes a color restoration verification step: comparing and verifying the generated color scheme with the colors of the archaeologically discovered fragments. The comparison is calculated using the color difference formula ΔEab, and a successful match is determined when ΔEab≤3.

[0020] Preferably, when the method is applied to the protection and restoration of ancient buildings, it outputs a visualization report containing the following content: 3D color scheme model and renderings from different perspectives; Explanation of the historical and cultural basis of the color scheme; Color weather resistance prediction and maintenance recommendations.

[0021] Compared with the prior art, the present invention has the following beneficial effects: 1. Multi-source data fusion improves color extraction accuracy: By collaboratively acquiring multispectral images (covering visible and near-infrared bands) and 3D point cloud data (density ≥100 points / square meter), combined with an improved U-Net network (including attention mechanism), the spatial distribution of building component colors is accurately extracted, reducing the error by more than 40% compared to traditional single-point sampling.1-56

[0022] 2. Standardized knowledge base supports cultural origin tracing and scientific color matching: Integrating over 1000 sets of traditional color samples, using CIE Lab spatial normalization (ΔE≤1.5) and labeling them with information such as historical period and regional style, a structured knowledge base was constructed. A color matching model based on the support vector machine algorithm improved the matching degree between the scheme and the historical and cultural background by 35%.

[0023] 3. Physical rendering and verification mechanisms enable realistic scene reproduction: Employing physically based rendering (PBR) technology combined with lighting simulation algorithms (considering solar azimuth and atmospheric transmittance), real-time color rendering under different ambient light conditions is achieved (frame rate ≥ 30fps), with a color difference ΔE*ab ≤ 3 compared to the actual scene. Through comparison with archaeological fragments and optimization using genetic algorithms, the accuracy of color scheme restoration is improved to over 90%. Attached Figure Description

[0024] Figure 1 This is an overall flowchart of a method for visualizing color matching of ancient buildings according to the present invention; Figure 2 This is a flowchart of multi-source data acquisition and processing for a visual color matching method for ancient buildings according to the present invention; Figure 3 This is a flowchart illustrating the color feature extraction and knowledge base construction process of a visual color matching method for ancient buildings according to the present invention. Figure 4 This is a flowchart illustrating the construction and optimization of a color matching model for a visual color matching method for ancient buildings according to the present invention. Figure 5 This is a flowchart of the three-dimensional rendering and verification process for a visual color matching method for ancient buildings according to the present invention. Detailed Implementation

[0025] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0026] A method for visualizing color schemes in ancient architecture, comprising: Step 1: Multi-source data acquisition: Acquire multispectral images, 3D point cloud data, historical documents, and regional cultural characteristic data of the ancient buildings; the multispectral images cover the visible and near-infrared bands with a resolution ≥200 dpi; the 3D point cloud data density is ≥100 points / square meter; the regional cultural characteristic data includes: Data on the symbolic meanings of local traditional colors were extracted from local chronicles and folklore documents using natural language processing technology. Climate and environmental data, including annual average temperature, humidity, and sunshine duration, are used for color weather resistance analysis.

[0027] Step 2, Color Feature Extraction: Color information is extracted from the multispectral image using a deep learning model, and the spatial color distribution of building components is obtained by combining it with 3D point cloud data. The deep learning model is an improved U-Net network that includes an attention mechanism module. The specific process of color extraction using the improved U-Net network includes: A residual connection module is used to enhance feature extraction capabilities, and skip connections are added between the encoder and decoder; By using spatial attention and channel attention mechanisms, irrelevant color information is suppressed, highlighting the color characteristics of architectural components.

[0028] Step 3: Construction of a historical color knowledge base: Integrate historical documents, archaeological data, and traditional craft information to establish a database of ancient building colors that includes different historical periods and regional styles. The database contains at least 1,000 sets of traditional color samples and their cultural background information. The processing of traditional color samples includes: The CIE Lab color space is used for standardized conversion, achieving an accuracy of ΔE≤1.5; Each color sample is labeled with its historical period, regional style, application area, and technological information.

[0029] Step 4: Color Matching Model Construction: Based on the historical color knowledge base, a color matching model is constructed using the support vector machine algorithm. The input of the model is the extracted color features and regional cultural features, and the output is the adapted traditional color matching scheme. The kernel function of the support vector machine algorithm is the radial basis function, the parameter γ ranges from 0.01 to 10, and the penalty parameter C ranges from 1 to 100. The optimal parameters are determined through cross-validation.

[0030] Step 5, 3D visualization rendering: The color scheme is mapped onto the 3D model of the ancient building, and the color effect under different ambient light conditions is displayed by combining the lighting simulation algorithm. The lighting simulation algorithm takes into account parameters such as solar azimuth angle and atmospheric transmittance. Physically based rendering (PBR) technology is used, and the technical parameters include: The resolution of the metallic texture and roughness texture should be ≥2048×2048; The sampling rate of the ambient occlusion (AO) map is ≥4×4; Supports real-time global illumination calculation with a frame rate of ≥30fps.

[0031] It also includes a color scheme optimization step: receiving user adjustment instructions through an interactive interface, and optimizing the color scheme by combining a genetic algorithm. The population size of the genetic algorithm is 50-100, the crossover probability is 0.6-0.8, and the mutation probability is 0.01-0.05. The interactive interface supports the following operations: The colors of building components are locally adjusted with an accuracy of ±5° for the H component, ±5% for the S component, and ±5% for the V component in the HSV color space. Preview the adjusted 3D rendering effect in real time.

[0032] It also includes a color restoration verification step: the generated color scheme is compared and verified with the colors of the fragments discovered in the archaeological site. The comparison is calculated using the color difference formula ΔEab, and a successful match is determined when ΔEab≤3.

[0033] When the method is applied to the protection and restoration of ancient buildings, it outputs a visualization report containing the following content: 3D color scheme model and renderings from different perspectives; Explanation of the historical and cultural basis of the color scheme; Color weather resistance prediction and maintenance recommendations.

[0034] Example: Color scheme restoration of the Meridian Gate of the Forbidden City based on a visual color scheme method for ancient architecture 1. Multi-source data acquisition Multispectral image acquisition: A multispectral camera covering the 400-1000nm wavelength band was used to photograph the Meridian Gate wall, glazed tiles, and other components at a resolution of 200dpi, acquiring spectral data containing pigment composition information. Visible light images were acquired simultaneously, with a focus on recording the color distribution of vermilion red (RGB 197, 27, 30) on the wall and bright yellow (RGB 255, 215, 0) on the glazed tiles.

[0035] 3D point cloud data acquisition: Using a Faro Focus S70 scanner, the 3D model of the Meridian Gate was acquired at a density of 120 points / square meter to accurately locate the spatial positions of components such as roof ornaments and brackets, providing a coordinate basis for color space mapping.

[0036] Cultural Feature Extraction: Using natural language processing technology, we extracted the Qing Dynasty court color system from the "Regulations and Rules for Engineering Practices of the Ministry of Works", such as "palace gates and corridors use vermilion doors and gold nails". Combined with climate data of Beijing area with an average annual temperature of 12℃ and sunshine duration of 2400 hours, we analyzed the color weather resistance requirements.

[0037] 2. Color Feature Extraction Deep learning model application: An improved U-Net network (including residual connections and spatial attention mechanisms) is used to process multispectral images. The encoder extracts color features through 7 layers of convolution, the decoder uses skip connections to recover spatial details, and the attention mechanism suppresses irrelevant information such as wall stains, highlighting the spectral characteristics of the glazed tiles (peak reflectance in the near-infrared band at 680nm).

[0038] Output the CIE Lab values ​​for each component: wall L*=32.5, a*=58.2, b*=42.1; glazed tile L*=78.3, a*=22.5, b*=65.4, and generate a color space distribution map by associating it with the 3D point cloud data.

[0039] 3. Construction of a Historical Color Knowledge Base Sample standardization: 120 sets of Qing Dynasty architectural polychrome paintings from the Palace Museum collection were integrated and transformed using CIE Lab space transformation (ΔE=1.2), with attributes such as "Qianlong period" and "royal palace" labeled. For example, the sample corresponding to the glazed tiles of the Meridian Gate was labeled as: "Bright yellow, L*=78±2, a*=23±1, b*=66±2, used for palace roofs, mineral pigments sulfur + lead oxide".

[0040] Cultural semantic annotation: Extract the symbolic meaning of "yellow as the central color" from "Zhuozhongzhi", and combine it with the regional color habit of "blue tiles and gray walls" in Beijing courtyard houses to construct a knowledge base that includes color symbols and craft techniques (such as "applying powder and applying gold").

[0041] 4. Color scheme model construction and optimization Support Vector Machine (SVM) training: The model was trained using color features of the Meridian Gate components (L*, a*, b* values ​​+ regional cultural vectors) as input and historical knowledge base samples as output, employing a radial basis function kernel (γ=0.5, C=10). Test set results show a 92% accuracy rate in matching color schemes for "Royal Palace" type buildings.

[0042] Interactive optimization: The color of the bracket sets is adjusted using a genetic algorithm with a population size of 50, a crossover probability of 0.7, and a mutation probability of 0.03. Users can adjust the original color scheme of the bracket sets (vermilion L*=28.3) to a deeper red (L*=25.1) that is closer to historical samples, and preview the 3D rendering effect in real time.

[0043] 5. 3D visualization rendering and verification Physical rendering implementation: PBR technology is used, with metallic textures (0.1 for glazed tiles, 0 for wood) and roughness textures (0.6 for walls, 0.4 for tiles) set to simulate the lighting conditions at 9:00 AM on the summer solstice in Beijing (solar azimuth angle 110°, atmospheric transmittance 0.85), and a rendering frame rate of 35fps.

[0044] Restoration Verification: The generated scheme was compared with the archaeological fragments of the East Wing Tower of the Meridian Gate (restored in 1956). The wall color ΔEab=2.3≤3, and the glazed tile ΔEab=1.8, indicating a successful match. Simultaneously, the historical accuracy of the color scheme was verified by referring to the record in the "Qing Dynasty Architectural Regulations" that "the eaves pillars are vermilion, and the brackets are blue-green."

[0045] 6. Visual report generation The output includes a 3D color scheme model of the Meridian Gate (including renderings of the front and side elevations), the cultural basis for the color scheme (such as "yellow belongs to earth, symbolizing the central imperial power"), and maintenance suggestions based on Beijing's climate ("the vermilion paint on the walls needs to be touched up every spring to prevent fading due to ultraviolet rays"). In summary, this invention provides a method for visualizing color matching in ancient architecture, achieving cross-scale data fusion: transforming the qualitative analysis of "regional culture and natural environment" into quantitative acquisition of multispectral images and 3D point clouds, enabling precise spatial positioning of color features. It also structures cultural semantics: based on the case of "red walls and yellow tiles of the Forbidden City," a standardized knowledge base containing historical periods and craft techniques is constructed, solving the problem of fragmented traditional color matching experience. Furthermore, through physically realistic rendering: combining the light analysis of "nighttime light transmittance of glass curtain walls," it extends to color simulation of ancient buildings under different climatic conditions, enhancing the credibility of the restoration.

[0046] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for visualizing color schemes in ancient architecture, characterized in that, Includes the following steps: Step 1: Multi-source data acquisition: Collect multispectral images, 3D point cloud data, historical documents, and regional cultural characteristic data of the ancient buildings; the multispectral images cover the visible and near-infrared bands with a resolution ≥200 dpi; the 3D point cloud data density ≥100 points / square meter; Step 2, Color Feature Extraction: Color information is extracted from the multispectral image using a deep learning model, and the spatial color distribution of building components is obtained by combining it with 3D point cloud data. The deep learning model is an improved U-Net network that includes an attention mechanism module. Step 3: Construction of a historical color knowledge base: Integrate historical documents, archaeological data, and traditional craft information to establish a database of ancient building colors that includes different historical periods and regional styles. The database contains at least 1,000 sets of traditional color samples and their cultural background information. Step 4: Color Matching Model Construction: Based on the historical color knowledge base, a color matching model is constructed using the support vector machine algorithm. The input of the model is the extracted color features and regional cultural features, and the output is the adapted traditional color matching scheme. Step 5, 3D visualization rendering: The color scheme is mapped onto the 3D model of the ancient building, and the color effect under different ambient light conditions is displayed by combining the lighting simulation algorithm. The lighting simulation algorithm takes into account parameters such as solar azimuth angle and atmospheric transmittance.

2. The method for visualizing color matching of ancient buildings according to claim 1, characterized in that: In the multi-source data acquisition step, the regional cultural characteristic data includes: Data on the symbolic meanings of local traditional colors were extracted from local chronicles and folklore documents using natural language processing technology. Climate and environmental data, including annual average temperature, humidity, and sunshine duration, are used for color weather resistance analysis.

3. The method for visualizing color matching of ancient buildings according to claim 1, characterized in that: The specific process of color extraction using the improved U-Net network in the color feature extraction step includes: A residual connection module is used to enhance feature extraction capabilities, and skip connections are added between the encoder and decoder; By using spatial attention and channel attention mechanisms, irrelevant color information is suppressed, highlighting the color characteristics of architectural components.

4. The method for visualizing color matching of ancient buildings according to claim 1, characterized in that: The processing of traditional color samples in the historical color knowledge base construction steps includes: The CIE Lab color space is used for standardized conversion, achieving an accuracy of ΔE≤1.5; Each color sample is labeled with its historical period, regional style, application area, and technological information.

5. The method for visualizing color matching of ancient buildings according to claim 1, characterized in that: In the color matching model construction step, the kernel function of the support vector machine algorithm is the radial basis function, the parameter γ ranges from 0.01 to 10, and the penalty parameter C ranges from 1 to 100. The optimal parameters are determined through cross-validation.

6. The method for visualizing color matching of ancient buildings according to claim 1, characterized in that: The 3D visualization rendering step employs Physically Based Rendering (PBR) technology, and the technical parameters include: The resolution of the metallic texture and roughness texture should be ≥2048×2048; The sampling rate of the ambient occlusion (AO) map is ≥4×4; Supports real-time global illumination calculation with a frame rate of ≥30fps.

7. The method for visualizing color matching of ancient buildings according to claim 1, characterized in that: It also includes a color scheme optimization step: receiving user adjustment instructions through an interactive interface, and optimizing the color scheme by combining a genetic algorithm. The population size of the genetic algorithm is 50-100, the crossover probability is 0.6-0.8, and the mutation probability is 0.01-0.

05.

8. The method for visualizing color matching of ancient buildings according to claim 7, characterized in that: The interactive interface supports the following operations: The colors of building components are locally adjusted with an accuracy of ±5° for the H component, ±5% for the S component, and ±5% for the V component in the HSV color space. Preview the adjusted 3D rendering effect in real time.

9. The method for visualizing color matching of ancient buildings according to claim 1, characterized in that: It also includes a color restoration verification step: the generated color scheme is compared and verified with the colors of the fragments discovered in the archaeological site. The comparison is calculated using the color difference formula ΔEab, and a successful match is determined when ΔEab≤3.

10. The method for visualizing color matching of ancient buildings according to claim 1, characterized in that: When the method is applied to the protection and restoration of ancient buildings, it outputs a visualization report containing the following content: 3D color scheme model and renderings from different perspectives; Explanation of the historical and cultural basis of the color scheme; Color weather resistance prediction and maintenance recommendations.

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