3D Transformer Augmentation for Viewpoint-Invariant Scene Learning

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Solution Overview

Problem

Existing transformer architectures for computer vision lack sufficient diversity in supervision data for multi-view geometry, leading to inefficiencies and sensitivity to calibration errors.

Innovation Solution

Implement geometric 3D data augmentation techniques to generate additional supervision data by projecting information from a pointcloud onto a virtual camera with a different viewpoint, encoding scene geometry at the input level, and using virtual cameras with ground-truth RGB images and depth maps to enforce viewpoint-invariant latent scene representations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If specialized architectures with loss-level geometric constraints are used, then geometric accuracy is improved, but computational speed and memory efficiency deteriorate

Engineering Contradiction:

Improvegeometric accuracyVSAvoidcomputational speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces traditional mechanical/computational approaches (cost volumes, bundle adjustment, epipolar losses) with a neural network-based latent scene representation that encodes geometric priors directly in the latent space, enabling faster inference without specialized architectures

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent changes the representation parameters by encoding multi-view geometry and scene structure into a compact latent scene representation, transforming the problem from explicit geometric computation to latent space reasoning, which improves both speed and generalization

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If specialized architectures with loss-level geometric constraints are used, then geometric accuracy is improved, but memory consumption increases

Engineering Contradiction:

Improvegeometric accuracyVSAvoidmemory consumption

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent extracts the essential geometric constraints from complex loss-level formulations and encodes them directly into the latent scene representation, removing the need for separate cost volumes and geometric constraint modules that consume significant memory

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent substitutes memory-intensive geometric computation structures (cost volumes, bundle adjustment objects) with a compact latent scene representation that maintains geometric accuracy while reducing memory footprint

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If generalist architectures encoding geometric priors at input level are used, then versatility is improved, but generalization power deteriorates due to lack of 3D supervision

Engineering Contradiction:

Improvetask versatilityVSAvoidgeneralization power

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent performs preliminary encoding of multi-view geometry and scene structure into the latent scene representation during training, preparing a rich geometric prior that enables the model to generalize to unseen viewpoints and tasks without requiring task-specific supervision

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent creates a universal latent scene representation that can serve multiple functions (depth estimation, view synthesis, scene understanding) by encoding comprehensive geometric priors, eliminating the need for task-specific architectures while maintaining strong generalization

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Measurement precision

If traditional correspondence-based stereo methods are used, then depth estimation accuracy is improved, but computational complexity increases

Engineering Contradiction:

Improvedepth estimation accuracyVSAvoidarchitectural complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent substitutes traditional correspondence-based stereo matching mechanisms (epipolar line constraints, cost volumes, pixel matching) with a neural network that reasons about depth and geometry through latent scene representation, simplifying the architectural complexity while maintaining accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12488483B2Geometric 3D augmentations for transformer architectures

Publication Date: 2025.12.02 TOYOTA JIDOSHA KK

AI summary

A method of generating additional supervision data to improve learning of a geometrically-consistent latent scene representation with a geometric scene representation architecture is provided. The method includes receiving, with a computing device, a latent scene representation encoding a pointcloud from images of a scene captured by a plurality of cameras each with known intrinsics and poses, generating a virtual camera having a viewpoint different from viewpoints of the plurality of cameras, projecting information from the pointcloud onto the viewpoint of the virtual camera, and decoding the latent scene representation based on the virtual camera thereby generating an RGB image and depth map corresponding to the viewpoint of the virtual camera for implementation as additional supervision data.