We introduce a novel 3D generative method, Generative 3D Reconstruction (G3DR) in ImageNet, capable of generating diverse and high-quality 3D objects from single images, addressing the limitations of existing methods. At the heart of our framework is a novel depth regularization technique that enables the generation of scenes with high-geometric fidelity. G3DR also leverages a pretrained language-vision model, such as CLIP, to enable reconstruction in novel views and improve the visual realism of generations. Additionally, G3DR designs a simple but effective sampling procedure to further improve the quality of generations. G3DR offers diverse and efficient 3D asset generation based on class or text conditioning. Despite its simplicity, G3DR is able to beat state-of-theart methods, improving over them by up to 22% in perceptual metrics and 90% in geometry scores, while needing only half of the training time. Code is available at https://github.com/preddy5/G3DR

翻译：我们提出了一种新颖的三维生成方法——ImageNet中的生成式三维重建（G3DR），能够从单张图像生成多样且高质量的三维物体，克服了现有方法的局限性。我们框架的核心是一种新颖的深度正则化技术，可生成具有高几何保真度的场景。G3DR还利用预训练的视觉-语言模型（如CLIP），以支持新视角下的重建，并提升生成结果的视觉真实感。此外，G3DR设计了一种简单而高效的采样流程，进一步改善生成质量。基于类别或文本条件，G3DR实现了多样且高效的三维资产生成。尽管方法简洁，G3DR在感知指标和几何分数上分别比当前最优方法提升高达22%和90%，同时训练时间仅需一半。代码已开源：https://github.com/preddy5/G3DR