# SDF-3DGAN: A 3D Object Generative Method Based on Implicit Signed Distance Function arXiv: https://arxiv.org/abs/2303.06821 ยท HTML: https://mlsanity.com/paper/2303.06821/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 41 references that are in the map. - First posted on arXiv 13 Mar 2023. ## Builds on (most cited first, 28 of 28) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018) - [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014) - [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019) - [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017) - [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019) - [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017) - [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017) - [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020) - [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021) - [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018) - [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021) - [pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2012.00926/) (2020) - [GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2007.02442/) (2020) - [Which Training Methods for GANs do actually Converge?](https://mlsanity.com/paper/1801.04406/) (2018) - [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling](https://mlsanity.com/paper/1610.07584/) (2016) - [StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis](https://mlsanity.com/paper/2110.08985/) (2021) - [SAL: Sign Agnostic Learning of Shapes from Raw Data](https://mlsanity.com/paper/1911.10414/) (2019) - [StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generation](https://mlsanity.com/paper/2112.11427/) (2021) - [Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild](https://mlsanity.com/paper/1911.11130/) (2019) - [SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization](https://mlsanity.com/paper/1912.07109/) (2019) - [CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis](https://mlsanity.com/paper/2110.09788/) (2021) - [Adversarial Generation of Continuous Images](https://mlsanity.com/paper/2011.12026/) (2020) - [Visual Object Networks: Image Generation with Disentangled 3D Representation](https://mlsanity.com/paper/1812.02725/) (2018) - [A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis](https://mlsanity.com/paper/2110.15678/) (2021) - [Patch-Based Image Inpainting with Generative Adversarial Networks](https://mlsanity.com/paper/1803.07422/) (2018) - [Generative Occupancy Fields for 3D Surface-Aware Image Synthesis](https://mlsanity.com/paper/2111.00969/) (2021) ## Built on by (most cited first, 7 of 7) - [Multimodal Spatial Reasoning in the Large Model Era: A Survey and Benchmarks](https://mlsanity.com/paper/2510.25760/) (2025) - [BrightDreamer: Generic 3D Gaussian Generative Framework for Fast Text-to-3D Synthesis](https://mlsanity.com/paper/2403.11273/) (2024) - [RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning](https://mlsanity.com/paper/2502.00848/) (2025) - [Image Anything: Towards Reasoning-coherent and Training-free Multi-modal Image Generation](https://mlsanity.com/paper/2401.17664/) (2024) - [SPGen: Spherical Projection as Consistent and Flexible Representation for Single Image 3D Shape Generation](https://mlsanity.com/paper/2509.12721/) (2025) - [Learning Effective NeRFs and SDFs Representations with 3D Generative Adversarial Networks for 3D Object Generation](https://mlsanity.com/paper/2309.16110/) (2023) - [G-NeLF: Memory- and Data-Efficient Hybrid Neural Light Field for Novel View Synthesis](https://mlsanity.com/paper/2409.05617/) (2024) ## Abstract (arXiv) In this paper, we develop a new method, termed SDF-3DGAN, for 3D object generation and 3D-Aware image synthesis tasks, which introduce implicit Signed Distance Function (SDF) as the 3D object representation method in the generative field. We apply SDF for higher quality representation of 3D object in space and design a new SDF neural renderer, which has higher efficiency and higher accuracy. To train only on 2D images, we first generate the objects, which are represented by SDF, from Gaussian distribution. Then we render them to 2D images and use them to apply GAN training method together with 2D images in the dataset. In the new rendering method, we relieve all the potential of SDF mathematical property to alleviate computation pressure in the previous SDF neural renderer. In specific, our new SDF neural renderer can solve the problem of sampling ambiguity when the number of sampling point is not enough, ie use the less points to finish higher quality sampling task in the rendering pipeline. And in this rendering pipeline, we can locate the surface easily. Therefore, we apply normal loss on it to control the smoothness of generated object surface, which can make our method enjoy the much higher generation quality. Quantitative and qualitative experiments conducted on public benchmarks demonstrate favorable performance against the state-of-the-art methods in 3D object generation task and 3D-Aware image synthesis task. Our codes will be released at https://github.com/lutao2021/SDF-3DGAN.