Pixel-wise residual pose estimation for monocular depth
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Solution Overview
Problem
Conventional systems face challenges in accurately reconstructing 3D environments using monocular cameras due to limited depth perception and the presence of dynamic objects, which affects the accuracy of depth and pose estimation in autonomous agents.
Innovation Solution
A self-supervised learning approach that generates depth and pose estimates using a depth network and pose network, respectively, with pixel-wise residual motion accounting for external motion, and an encoder-decoder architecture with 3D packing and unpacking blocks to improve depth estimation and scene reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If monocular cameras are used for scene reconstruction, then device cost is reduced, but depth estimation accuracy deteriorates
Solution Approach 1:
The patent replaces the mechanical/optical approach of stereo cameras or LIDAR with a computational method that uses monocular image sequences. By substituting the need for additional hardware with algorithmic processing, the system achieves depth estimation accuracy comparable to more expensive systems while using only a single camera.
Solution Approach 2:
The system uses the monocular camera's own image sequence to reconstruct the scene, leveraging temporal information and self-supervised learning. The camera system serves itself by processing its own outputs across time to derive depth and pose information without requiring external sensors or ground truth data.
2Productivity
If conventional pose estimation is used, then processing speed is maintained, but accuracy in dynamic environments deteriorates
Solution Approach 1:
The patent segments the pose estimation task into two components: a global pose estimate from conventional methods and a per-pixel residual pose correction. This segmentation allows the system to maintain the speed of conventional methods while adding the accuracy of pixel-level refinements to handle dynamic objects.
Solution Approach 2:
The system applies different quality levels to different parts of the image. Global pose estimation provides a base accuracy for the entire scene, while per-pixel residual pose estimates provide enhanced accuracy locally for regions containing dynamic objects, allowing the system to optimize both speed and accuracy where needed.
3Measurement precision
If pixel-wise residual pose estimation is added, then depth estimation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies pixel-wise residual pose estimation selectively rather than universally. The per-pixel refinement is applied only when and where needed, such as in regions with dynamic objects or when high accuracy is required, rather than processing the entire image at full resolution always, thus reducing overall computational complexity.
Solution Approach 2:
The system performs preliminary global pose estimation and depth prediction before applying the computationally intensive per-pixel residual correction. This preliminary action handles the bulk of the processing with simpler methods, reserving the complex pixel-wise refinement for specific regions or as a final enhancement step.
Data Source
AI summary
A method for scene reconstruction includes generating a depth estimate and a first pose estimate from a current image. The method also includes generating a second pose estimate based on the current image and one or more previous images in a sequence of images. The method further includes generating a warped image by warping each pixel in the current image based on the depth estimate, the first pose estimate, and the second pose estimate. The method still further includes controlling an action of an agent based on the second warped image.