Although LEGO sets have entertained generations of children and adults, the challenge of designing customized builds matching the complexity of real-world or imagined scenes remains too great for the average enthusiast. In order to make this feat possible, we implement a system that generates a LEGO brick model from 2D images. We design a novel solution to this problem that uses an octree-structured autoencoder trained on 3D voxelized models to obtain a feasible latent representation for model reconstruction, and a separate network trained to predict this latent representation from 2D images. LEGO models are obtained by algorithmic conversion of the 3D voxelized model to bricks. We demonstrate first-of-its-kind conversion of photographs to 3D LEGO models. An octree architecture enables the flexibility to produce multiple resolutions to best fit a user's creative vision or design needs. In order to demonstrate the broad applicability of our system, we generate step-by-step building instructions and animations for LEGO models of objects and human faces. Finally, we test these automatically generated LEGO sets by constructing physical builds using real LEGO bricks.
翻译:虽然LEGO各组装饰了几代儿童和成年人,但设计符合现实世界或想象中场景复杂性的定制建筑的挑战对于普通爱好者来说仍然太大。为了让这一成就成为可能,我们实施了一个从 2D 图像生成LEGO砖模型的系统。我们设计了一个新颖的解决方案,利用3D 氧化模型培训的奥氏结构自动编码器,为模型重建找到可行的潜在代表,并建立一个单独的网络,从 2D 图像中预测这种潜在代表。LEGO 模型是通过将3D 氧化模型转换成砖的算法获得的。我们用3D LEGO 模型进行首次实物转换。Aoctree结构使得能够产生多种分辨率,以最好地满足用户的创造性愿景或设计需要。为了证明我们的系统的广泛适用性,我们为LEGO 的物体和人脸模型制作了逐步的指令和动画。最后,我们通过使用真正的LEGO 砖块建造物理建筑来测试这些自动生成的LEGO 。