3D Scene Motion Estimation With Temporal Embeddings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Motion estimation and view prediction in autonomous systems face challenges due to motion ambiguity and varying lighting conditions, making it difficult to accurately represent scenes and predict object movements.
Innovation Solution
A system utilizing machine-learning models to estimate the motion and volumetric representation of a scene, comprising a motion predictor, motion field predictor, and space/time field predictor, to generate synthetic images of the scene at a target time or viewing direction, using trained neural networks to determine motion features, locations, and image properties of 3D points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional motion estimation methods are used, then the system can process scenes with simple motion patterns, but it fails to accurately represent scenes with motion ambiguity and varying lighting conditions
Solution Approach 1:
The patent transforms the motion estimation problem from direct pixel-level analysis to a latent feature space representation. By learning temporal embeddings that capture motion patterns in a compressed parameter space, the system can generalize to unseen motion scenarios and lighting conditions, resolving the contradiction between precision and adaptability.
Solution Approach 2:
The patent introduces temporal embedding vectors as an intermediary representation between input image sequences and motion estimation outputs. These embeddings serve as a mediator that captures essential motion characteristics while filtering out noise from varying lighting conditions, enabling accurate motion prediction despite environmental variations.
2Measurement precision
If complex machine-learning models are used to improve motion estimation accuracy, then prediction precision improves, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts only the essential temporal motion characteristics from input image sequences by computing temporal embeddings that capture dominant motion patterns. This extraction approach avoids processing all image details, reducing computational complexity while maintaining accuracy for motion estimation tasks.
Solution Approach 2:
The patent changes the parameter representation from high-dimensional image pixels to compact temporal embedding vectors. This parameter transformation reduces the computational burden of processing while preserving the essential motion information needed for accurate scene representation and prediction.
3Measurement precision
If the system processes all image details to ensure accurate motion estimation, then measurement precision improves, but processing speed decreases
Solution Approach 1:
The patent extracts only the necessary temporal motion features from input sequences through learned embeddings, discarding redundant visual details. This selective extraction maintains motion prediction accuracy while significantly reducing processing time and computational resource requirements.
Solution Approach 2:
The patent performs preliminary computation of temporal embeddings from input image sequences before executing the main motion estimation and view prediction tasks. This preliminary action pre-processes and compresses the input data into essential motion characteristics, accelerating subsequent processing steps while preserving accuracy.
Data Source
AI summary
Described herein are systems, methods, and instrumentalities associated with estimating the motions of multiple 3D points in a scene and predicting a view of scene based on the estimated motions. The tasks may be accomplished using one or more machine-learning (ML) models. A first ML model may be used to predict motion-embedding features for a temporal state of a scene, based on motion-embedding features for previous states. A second ML model may be used to predict a motion field representing displacement or deformation of the multiple 3D points from a source time to a target time. Then, a third ML model may be used to predict respective image properties of the 3D points based on their updated locations at the target time and/or a viewing direction. An image of the scene at the target time may then be generated based on the predicted image properties of the 3D points.