In this technical report, we present a solution for 3D object generation of ICCV 2023 OmniObject3D Challenge. In recent years, 3D object generation has made great process and achieved promising results, but it remains a challenging task due to the difficulty of generating complex, textured and high-fidelity results. To resolve this problem, we study learning effective NeRFs and SDFs representations with 3D Generative Adversarial Networks (GANs) for 3D object generation. Specifically, inspired by recent works, we use the efficient geometry-aware 3D GANs as the backbone incorporating with label embedding and color mapping, which enables to train the model on different taxonomies simultaneously. Then, through a decoder, we aggregate the resulting features to generate Neural Radiance Fields (NeRFs) based representations for rendering high-fidelity synthetic images. Meanwhile, we optimize Signed Distance Functions (SDFs) to effectively represent objects with 3D meshes. Besides, we observe that this model can be effectively trained with only a few images of each object from a variety of classes, instead of using a great number of images per object or training one model per class. With this pipeline, we can optimize an effective model for 3D object generation. This solution is one of the final top-3-place solutions in the ICCV 2023 OmniObject3D Challenge.

使用3D生成对抗网络（GANs）来学习有效的NeRFs和SDFs表示，以生成复杂、纹理和高保真度的3D物体。该方法结合标签嵌入和颜色映射，并借助快速几何感知的3D GANs作为骨干模型，能够同时在不同分类任务上训练模型，优化生成高保真度合成图像所需的神经辐射场（NeRFs）的表示，并通过优化有符号距离函数（SDFs）来有效表示3D物体网格。此解决方案在ICCV 2023 OmniObject3D Challenge中获得前3名。

Learning Effective NeRFs and SDFs Representations with 3D Generative
  Adversarial Networks for 3D Object Generation: Technical Report for ICCV 2023
  OmniObject3D Challenge

使用3D生成对抗网络学习有效的NeRFs和SDFs表示，用于3D物体生成：ICCV 2023 OmniObject3D挑战的技术报告