Neural Scene Representation for Geometry-Free Novel View Synthesis
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Solution Overview
Problem
Existing methods for inferring 3D scene representations from images require precise camera poses and lengthy processing times, and conventional novel view synthesis techniques are expensive and inefficient, especially for unsupervised learning and handling complex scenes with multiple objects.
Innovation Solution
A machine-learned encoder-decoder model framework using self-attention transformers processes input images to generate a latent scene representation, enabling efficient novel view synthesis without explicit geometry, leveraging large databases of images with approximate geolocations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If explicit 3D representations (textured meshes, voxels, octrees) are reconstructed from images, then interactive rendering efficiency is improved, but expensive and fragile reconstruction processes are required and discrete representations with limited resolution are produced
Solution Approach 1:
The patent replaces traditional geometric reconstruction mechanisms (mesh generation, voxelization, octree construction) with a neural network-based implicit representation system. The neural radiance field learns continuous 3D scene geometry and appearance directly from images, eliminating the need for explicit geometric primitives and their associated complex reconstruction pipelines while achieving interactive rendering speeds.
2Manufacturing precision
If Neural Radiance Fields (NeRF) are trained for each scene, then implicit continuous representations are achieved, but very expensive training and rendering procedures are required with many MLP evaluations for volume rendering
Solution Approach 1:
The patent performs preliminary action by pre-training a neural radiance field model on a large database of synthetic images from multiple object categories before deployment. This pre-trained model serves as a prior that can be quickly adapted to new scenes, avoiding the need for expensive per-scene training while maintaining continuous representation accuracy.
Solution Approach 2:
The patent changes the parameterization approach by using a hybrid representation that combines learned object priors with scene-specific adaptations. Instead of training full NeRF models from scratch for each scene, the system adjusts key parameters and adapts the pre-trained model to new scenes, significantly reducing computational cost while maintaining continuous geometry accuracy.
3Adaptability or versatility
If Light Field Networks (LFN) with meta-learning are used, then a prior over scene representations is learned, but precise camera poses are required and expensive auto-decoder optimization is needed for each new scene
Solution Approach 1:
The patent uses copying by training the neural radiance field on synthetic images generated from 3D object models with known poses. The system learns to generalize from these synthetic examples to real scenes without requiring precise camera pose measurements, effectively copying knowledge from the synthetic training domain to the real-world application domain.
4Adaptability or versatility
If supervised learning with explicit supervision is used for object-centric understanding, then object categories can be learned, but carefully annotated data at large scale is required and rare or novel object categories are problematic
Solution Approach 1:
The patent performs preliminary action by pre-training the neural radiance field on large-scale synthetic datasets containing diverse object categories with known 3D geometries and appearances. This pre-training establishes object priors that enable the system to handle rare and novel object categories in deployment without requiring annotated training data for each specific category.
Solution Approach 2:
The system uses self-service by generating its own training data through synthetic scene rendering from 3D object models. The neural radiance field learns to represent objects by self-supervised learning from these generated images, eliminating the need for manual annotation while achieving robust object-centric understanding across diverse categories.
Data Source
AI summary
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.