Scene Flow Estimation via Depth-Motion Feature Integration
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Solution Overview
Problem
Existing scene flow estimation methods face challenges in accurately estimating three-dimensional scene flow from monocular images, particularly due to depth uncertainty and the need for multi-view geometry.
Innovation Solution
A processor-implemented scene flow estimation method that receives feature pyramids from encoding consecutive frames of an input image, extracts depth and motion features, generates an overall feature, and estimates scene flow, optical flow, and depth using a combination of convolution operations, warping, correlation, and self-attention mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-view geometry and Bayesian neural network are used to estimate scene flow, then depth uncertainty is reduced and accuracy is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent extracts depth estimation as a separate sub-task from scene flow estimation. By using a dedicated depth estimation network that operates independently on monocular images, the system obtains depth maps without requiring multi-view geometry or Bayesian neural networks, thereby reducing system complexity while maintaining depth accuracy
Solution Approach 2:
The patent segments the scene flow estimation process into two independent modules: depth estimation and optical flow estimation. This segmentation allows each module to be optimized separately, with depth estimation using simple monocular cues and optical flow using standard 2D techniques, avoiding the need for complex integrated multi-view systems
2Device complexity
If monocular depth estimation based on self-supervised learning is used, then device complexity is reduced, but depth uncertainty increases
Solution Approach 1:
The patent merges the outputs of depth estimation and optical flow estimation through a unified scene flow computation module. By combining the depth map from monocular estimation with the optical flow from 2D analysis, the system compensates for depth uncertainty through the complementary information provided by motion cues, thereby improving reliability without increasing device complexity
Solution Approach 2:
The patent implements a feedback mechanism where the estimated scene flow is used to refine both depth and optical flow estimates through iterative optimization. The loss function incorporates constraints from both depth consistency and optical flow consistency, allowing the system to adjust estimates to reduce depth uncertainty while maintaining computational efficiency
3Measurement precision
If feature pyramids with multiple decoding levels are used, then scene flow estimation accuracy is improved, but computational time and processing complexity increase
Solution Approach 1:
The patent performs preliminary depth estimation and feature extraction at multiple scales before final scene flow computation. By pre-processing the input image to generate depth maps and multi-scale features in advance, the system reduces the computational burden during the main scene flow estimation phase, thereby improving accuracy without excessive time penalty
Data Source
AI summary
A scene flow estimation method and apparatus are provided. The scene flow estimation method includes receiving a first feature pyramid and a second feature pyramid by encoding a first frame and a second frame of an input image through the same encoder, extracting a depth feature based on the first feature pyramid, extracting a motion feature based on the first feature pyramid and the second feature pyramid, generating an overall feature based on the depth feature and the motion feature, and estimating a scene flow based on the overall feature.