Continuous Scene Flow Estimation With Neural ODE Attention
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current scene flow estimation techniques, particularly those using 3D point clouds, face challenges such as inaccurate representation of continuous motion due to discrete timestamps, inability to capture implicit point intentions and goals, limited temporal context, and high computational complexity, especially in complex dynamic scenes.
Innovation Solution
Utilizing a neural ordinary differential equation (ODE) and an ODE solver to model scene flow as a continuous-time dynamical system, combined with a spatial-temporal attention module to segment dynamic and static points, enabling accurate and efficient estimation of scene flow by focusing on dynamic points and leveraging temporal context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If discrete timestamp methods are used for scene flow estimation, then computational complexity is reduced, but accuracy of continuous motion representation deteriorates
Solution Approach 1:
The patent applies continuous-time dynamical systems to model scene flow, replacing discrete timestamp methods with continuous temporal modeling. This allows the system to represent motion continuously over time while maintaining computational tractability through neural ODE formulations, resolving the contradiction between computational complexity and motion representation accuracy.
Solution Approach 2:
The patent changes the temporal parameter representation from discrete timestamps to continuous time variables. By using neural ordinary differential equations that operate in continuous time, the system transforms the parameter domain while maintaining computational feasibility through modern deep learning techniques, thus improving motion representation without prohibitive computational cost.
2Loss of information
If all points in the scene are processed, then complete scene flow information is obtained, but computational complexity increases
Solution Approach 1:
The patent extracts and processes only the essential dynamic information from the scene using neural ODEs that automatically focus on changing patterns. By formulating the problem as continuous-time dynamical system estimation, the method extracts meaningful flow information without requiring exhaustive processing of all scene points, thus reducing computational complexity while preserving important motion information.
Solution Approach 2:
The patent applies dynamic modeling through neural ordinary differential equations that adaptively process scene information based on actual motion patterns. The continuous-time formulation allows the system to focus computational resources on dynamically changing regions and patterns rather than uniformly processing all points, achieving efficient scene flow estimation.
3Ease of manufacture
If 2D image sequences are used for scene flow estimation, then data acquisition is simplified, but accuracy of 3D motion recovery deteriorates
Solution Approach 1:
The patent transitions from 2D image sequence analysis to 3D point cloud processing, adding the depth dimension to the input data. By using 3D spatial information from point clouds and applying continuous-time dynamical systems in three-dimensional space, the system recovers accurate 3D motion while maintaining practical data acquisition through modern 3D sensing technologies.
Data Source
AI summary
Certain aspects of the present disclosure provide flow estimation techniques for dynamic scenes. A method generally includes obtaining a first time series sequence of sample sets of a scene (including a first and a second set of samples) over a first period of time associated with a reference time; processing, with a spatial-temporal attention module, the first time series sequence, to generate a modified first time series sequence of sample sets of the scene comprising the first set and not the second set of samples; processing, with a first neural ordinary differential equation (ODE) and a first ODE solver, the modified first time series sequence to predict a second time series sequence of sample sets; and generating a first output indicating a predicted flow of the scene from the reference time to a first selected time based on the modified first time series sequence and the second time series sequence.