18013983. Geometry-Free Neural Scene Representations Through Novel-View Synthesis simplified abstract (Google LLC)

Geometry-Free Neural Scene Representations Through Novel-View Synthesis

Organization Name

Google LLC

Inventor(s)

Seyed Mohammad Mehdi Sajjadi of Berlin (DE)

Henning Meyer of Berlin (DE)

Etienne François Régis Pot of Berlin (DE)

Urs Michael Bergmann of Berlin (DE)

Klaus Greff of Berlin (DE)

Noha Radwan of Zurich (CH)

Suhani Deepak-Ranu Vora of San Mateo CA (US)

[[:Category:Mario Lu�i� of Zurich (CH)|Mario Lu�i� of Zurich (CH)]][[Category:Mario Lu�i� of Zurich (CH)]]

Daniel Christopher Duckworth of Berlin (DE)

Thomas Allen Funkhouser of Menlo Park CA (US)

Andrea Tagliasacchi of Victoria, BC (CA)

Geometry-Free Neural Scene Representations Through Novel-View Synthesis - A simplified explanation of the abstract

This abstract first appeared for US patent application 18013983 titled 'Geometry-Free Neural Scene Representations Through Novel-View Synthesis

Simplified Explanation

The present disclosure introduces machine learning models that can create geometry-free neural scene representations using efficient object-centric novel-view synthesis. One key aspect is a framework where an encoder model processes RGB images to generate a latent scene representation for a decoder model to synthesize images based on target poses in a single pass. By utilizing transformers instead of convolutional or MLP networks, the encoder can learn an attention model to extract 3D scene information from a few images for rendering novel views accurately.

• Efficient object-centric novel-view synthesis using machine learning models
• Encoder model processes RGB images to create a latent scene representation
• Decoder model synthesizes images based on target poses in a single pass
• Transformers enable the encoder to learn an attention model for accurate rendering

Potential Applications

This technology could be applied in virtual reality, augmented reality, gaming, and computer graphics for generating realistic scenes and environments.

Problems Solved

1. Efficient generation of novel views without explicit geometry 2. Accurate rendering of scenes with correct projections, parallax, and occlusions

Benefits

1. Simplified scene representation generation 2. Improved image synthesis based on target poses 3. Enhanced attention model learning for 3D scene information extraction

Potential Commercial Applications

"Efficient Object-Centric Novel-View Synthesis for Virtual Reality and Gaming"

Possible Prior Art

Prior art in the field of computer vision and machine learning may include research on image synthesis, scene representation, and attention mechanisms in neural networks.

How does this technology compare to existing methods for scene representation and image synthesis in terms of efficiency and accuracy?

This article does not provide a direct comparison with existing methods in the field. Further research or experimentation may be needed to evaluate the performance of this technology against current approaches.

What are the limitations or constraints of using transformers for scene representation and image synthesis in this context?

The article does not address any potential limitations or constraints of using transformers for this specific application. Future studies could explore any challenges or drawbacks associated with this approach.

Original Abstract Submitted

Provided are machine learning models that generate geometry-free neural scene representations through efficient object-centric novel-view synthesis. In particular, one example aspect of the present disclosure provides a novel framework in which an encoder model (e.g., an encoder transformer network) processes one or more RGB images (with or without pose) to produce a fully latent scene representation that can be passed to a decoder model (e.g., a decoder transformer network). Given one or more target poses, the decoder model can synthesize images in a single forward pass. In some example implementations, because transformers are used rather than convolutional or MLP networks, the encoder can learn an attention model that extracts enough 3D information about a scene from a small set of images to render novel views with correct projections, parallax, occlusions, and even semantics, without explicit geometry.