基于图片的彩色浮雕3D打印模型生成数据

通过构建一个包含大量常规2D彩色图片及其对应彩色浅浮雕3D打印模型的大规模配对数据集，可以为深度学习模型提供训练基础，使其学习从单张图像的颜色和内容中推断出合适的深度层次并生成带有纹理的几何体。这一数据集主要适用于个性化纪念品定制、艺术装饰品创作、辅助视觉障碍人士感知图像的触觉艺术以及建筑装饰等领域。利用该数据训练出的模型，能够让用户通过上传任意一张彩色照片，就能一键生成一个既有立体感又保留了原图色彩的可3D打印浮雕模型，解决了传统浮雕制作依赖手工雕刻、耗时且成本高昂，以及普通用户无法将2D图像立体化的问题。基于单张图片生成彩色浮雕3D打印模型，旨在将2D图像转化为带色彩的实体艺术品。具体过程包括：（1）数据收集：用户提供一张彩色图片（I_rgb），如风景照、人像或艺术画作。（2）数据处理：将输入的彩色图片送入一个专门用于单目深度估计的深度学习模型，该模型分析图像内容并预测出一个像素级的高度图。该高度图通过公式 H_map = Encoder_depth(I_rgb) 提取，其中 H_map 为预测的高度图，Encoder_depth 为深度预测编码器。（3）模型构建：此步骤将2D信息转化为3D几何与颜色。首先，根据预测的高度图（H_map）程序化地生成一个网格平面，并通过置换贴图（Displacement Mapping）等技术将高度信息转化为网格的Z轴位移，形成浮雕的几何外形。然后，将原始的彩色图片（I_rgb）作为纹理，精确地贴到生成的浮雕表面上。整个彩色浮雕的生成过程可视为一个解码器完成，即 M_colored_relief = Decoder_relief(I_rgb)，其中Decoder_relief为解码器。关键指标为均方根误差（RMSE）和峰值信噪比（PSNR）。此方法适用于将任何2D图片快速、自动地转化为可供全彩3D打印机输出的彩色浮雕模型，极大地降低了浮雕艺术的创作门槛。

Building a large-scale paired dataset consisting of numerous standard 2D color images and their corresponding colored low-relief 3D printing models can provide a robust training foundation for deep learning models, enabling these models to learn to infer appropriate depth hierarchies from the color and content of a single image and generate textured geometric models. This dataset is primarily applicable to scenarios including customized souvenir production, artistic decorative creation, tactile art for assisting visually impaired individuals to perceive images, and architectural decoration.

Models trained on this dataset enable users to upload any color photo and generate a 3D-printable relief model that retains both the three-dimensional effect and the original image's colors with just one click. This solves the issues that traditional relief production relies on manual carving, which is time-consuming and cost-intensive, and ordinary users are unable to convert 2D images into three-dimensional forms. The task of generating colored relief 3D printing models from a single image aims to convert 2D images into colored physical artworks. The specific process includes:
1. Data Collection: Users provide a color image (I_rgb), such as landscape photographs, portraits, or artistic paintings.
2. Data Processing: The input color image is fed into a deep learning model specialized for monocular depth estimation. This model analyzes the image content and predicts a pixel-level height map, which is extracted via the formula: H_map = Encoder_depth(I_rgb), where H_map denotes the predicted height map and Encoder_depth represents the depth prediction encoder.
3. Model Construction: This step converts 2D information into 3D geometry and color. First, a grid plane is programmatically generated based on the predicted height map (H_map), and height information is converted into Z-axis displacement of the grid through techniques such as Displacement Mapping to form the geometric outline of the relief. Subsequently, the original color image (I_rgb) is used as a texture and accurately mapped onto the surface of the generated relief. The entire colored relief generation process can be treated as completed by a single decoder, expressed as M_colored_relief = Decoder_relief(I_rgb), where Decoder_relief refers to the decoder. The key evaluation metrics are Root Mean Squared Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR).

This method enables the rapid and automatic conversion of any 2D image into a colored relief model compatible with full-color 3D printers, significantly lowering the creation threshold for relief art.

搜集汇总

背景与挑战

背景概述

该数据集包含3382条CSV格式记录，用于训练深度学习模型从单张彩色图片生成彩色浮雕3D打印模型，数据结构包括图片、高度图和模型文件路径等关键字段。它主要应用于个性化纪念品和艺术装饰领域，通过自动化流程降低传统浮雕制作的门槛和成本，提升2D图像立体化的效率。