Nvidia corporation (20240104842). ENCODER-BASED APPROACH FOR INFERRING A THREE-DIMENSIONAL REPRESENTATION FROM AN IMAGE simplified abstract

ENCODER-BASED APPROACH FOR INFERRING A THREE-DIMENSIONAL REPRESENTATION FROM AN IMAGE

Organization Name

nvidia corporation

Inventor(s)

Koki Nagano of Playa Vista CA (US)

Alexander Trevithick of Mamaroneck NY (US)

Chao Liu of Pittsburgh PA (US)

Eric Ryan Chan of Alameda CA (US)

Sameh Khamis of Alameda CA (US)

Michael Stengel of Hayward CA (US)

Zhiding Yu of Santa Clara CA (US)

ENCODER-BASED APPROACH FOR INFERRING A THREE-DIMENSIONAL REPRESENTATION FROM AN IMAGE - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240104842 titled 'ENCODER-BASED APPROACH FOR INFERRING A THREE-DIMENSIONAL REPRESENTATION FROM AN IMAGE

Simplified Explanation

The abstract describes a method for generating a 3D representation of a 2D image using an encoder-based model trained on synthetic data generated by a pre-trained 3D generative model.

• The encoder-based model is trained to infer the 3D representation using synthetic training data.
• The pre-trained model produces a 3D representation and a corresponding 2D rendering for training the encoder-based model.
• The encoder-based model can be used for tasks like estimating a triplane representation, neural radiance field, mesh, depth map, 3D key points, etc.
• In a specific embodiment, the encoder-based model predicts a triplane representation that can be rendered by a volume renderer to generate an output image of the 3D scene.

Potential Applications

This technology can be applied in various fields such as computer vision, augmented reality, virtual reality, gaming, and 3D modeling.

Problems Solved

This technology solves the problem of converting 2D images into accurate 3D representations, which can be useful for various applications like object recognition, scene understanding, and immersive experiences.

Benefits

The benefits of this technology include improved accuracy in 3D representation generation, enhanced visualizations, better understanding of complex scenes, and potential for creating realistic virtual environments.

Potential Commercial Applications

Potential commercial applications of this technology include 3D content creation tools, virtual try-on solutions for e-commerce, interactive gaming experiences, and virtual tours for real estate.

Possible Prior Art

One possible prior art could be the use of neural networks for image-to-3D reconstruction, but this specific approach of training an encoder-based model on synthetic data generated by a pre-trained 3D generative model may be novel.

How does this technology compare to existing methods for image-to-3D reconstruction?

This article does not provide a direct comparison with existing methods for image-to-3D reconstruction. Further research or a comparative study would be needed to evaluate the performance and efficiency of this technology compared to other approaches.

What are the limitations of using synthetic training data for training the encoder-based model?

The article does not discuss the potential limitations or challenges of using synthetic training data. It would be important to understand how well the model generalizes to real-world data and whether there are any biases or inaccuracies introduced by the synthetic training data.

Original Abstract Submitted

a method for generating, by an encoder-based model, a three-dimensional (3d) representation of a two-dimensional (2d) image is provided. the encoder-based model is trained to infer the 3d representation using a synthetic training data set generated by a pre-trained model. the pre-trained model is a 3d generative model that produces a 3d representation and a corresponding 2d rendering, which can be used to train a separate encoder-based model for downstream tasks like estimating a triplane representation, neural radiance field, mesh, depth map, 3d key points, or the like, given a single input image, using the pseudo ground truth 3d synthetic training data set. in a particular embodiment, the encoder-based model is trained to predict a triplane representation of the input image, which can then be rendered by a volume renderer according to pose information to generate an output image of the 3d scene from the corresponding viewpoint.