基于图片的宠物类别3D打印模型生成数据

通过构建一个包含大量不同类别宠物、且均为水密性的3D打印模型及其对应的单张或多张渲染图片的大规模配对数据集，可以为深度学习模型提供训练基础，使其学习从二维宠物图像生成完整的三维几何。这一数据集主要适用于桌面游戏棋子和微缩模型的定制、个性化的宠物模型快速制作、虚拟化身的实体化打印以及内容创作等领域。利用该数据训练出的模型，能够让用户通过上传一张照片来生成一个可直接用于3D打印的对应模型，解决了从零开始制作三维模型技术难度极高、周期漫长且成本昂贵的问题。基于单张图片生成特定类别（如宠物模型）的可3D打印模型，旨在让三维创作大众化。具体过程包括：（1）数据收集：用户提供一张包含清晰宠物主体的RGB图片（I_rgb）（2）数据处理：将输入的图片送入一个在宠物图像上经过优化的预训练图像编码器。特征向量通过公式 F_image = Encoder_image(I_rgb) 提取，其中 F_image 为图像特征向量，Encoder_image 为图像编码器。（3）模型构建：使用提取的图像特征向量作为输入，设计并搭建一个专注于宠物三维模型生成的深度解码模型。该模型从特征中推断并生成宠物模型的隐式三维表示。根据公式 SDF = Decoder_3D(F_image) 从图像特征中解码出三维宠物模型，其中 SDF 为三维模型，Decoder_3D 为三维形状解码器。关键的评估指标包括交并比（Intersection over Union, IoU）和倒角距离（Chamfer Distance, CD）。此方法适用于从单张照片快速生成个性化的宠物模型，极大地降低了三维宠物模型的3D打印门槛。

By constructing a large-scale paired dataset comprising water-tight 3D printed models of various pet categories and their corresponding single or multiple rendered images, this dataset can serve as a training foundation for deep learning models, enabling them to learn to generate complete 3D geometry from 2D pet images.

This dataset is primarily applicable to fields including customization of tabletop game pieces and miniatures, rapid fabrication of personalized pet models, physical printing of virtual avatars, and content creation.

Models trained with this dataset allow users to generate a 3D-printable corresponding model by uploading a single photo, addressing the issues of extremely high technical difficulty, long production cycles, and high costs associated with creating 3D models from scratch.

Generating category-specific (e.g., pet models) 3D-printable models from a single image aims to democratize 3D creation. The specific workflow is as follows:
(1) Data Collection: The user provides an RGB image (I_rgb) featuring a clear pet subject.
(2) Data Processing: The input image is fed into a pre-trained image encoder optimized on pet image datasets. The image feature vector is extracted using the formula $F_{ ext{image}} = ext{Encoder}_{ ext{image}}(I_{ ext{rgb}})$, where $F_{ ext{image}}$ denotes the image feature vector and $ ext{Encoder}_{ ext{image}}$ is the image encoder.
(3) Model Construction: Taking the extracted image feature vector as input, a deep decoding model dedicated to generating 3D pet models is designed and built. This model infers and generates an implicit 3D representation of the pet model from the features. The 3D pet model is decoded from the image feature vector via the formula $SDF = ext{Decoder}_{3D}(F_{ ext{image}})$, where $SDF$ represents the 3D model and $ ext{Decoder}_{3D}$ is the 3D shape decoder.

Key evaluation metrics include Intersection over Union (IoU) and Chamfer Distance (CD).

This method enables rapid generation of personalized pet models from a single photo, significantly lowering the threshold for 3D printing of 3D pet models.

搜集汇总

背景与挑战

背景概述

该数据集是一个包含5841条记录的CSV格式数据，专门用于从二维宠物图像生成三维可打印模型，数据结构涵盖RGB图片、特征向量和3D模型路径，并采用IoU和CD作为评估指标。其核心特点是支持深度学习模型训练，实现从单张照片快速生成个性化宠物3D模型，应用于桌面游戏、虚拟化身实体化等领域，显著降低了3D打印的技术门槛和成本。