Video Depth Estimation via Deformation Splines and Geometric Optimization
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Solution Overview
Problem
Existing algorithms for dense video reconstruction, particularly those using Structure from Motion (SFM) and Multi-view Stereo (MVS), struggle with accurately and consistently estimating depth maps from monocular videos, especially in dynamic scenes with moving objects and in casual, handheld captures with noise and motion blur.
Innovation Solution
A robust consistent video depth estimation algorithm that integrates a learning-based depth prior, utilizing a convolutional neural network for single-image depth estimation, with geometric optimization. This algorithm employs flexible deformation-splines for low-frequency alignment and geometry-aware depth filtering for high-frequency alignment, allowing for joint estimation of camera poses and refined depth maps without requiring precise initial camera poses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SFM and MVS algorithms are used for dense reconstruction, then depth estimation can be performed, but accuracy and consistency deteriorate in dynamic scenes and casual captures
Solution Approach 1:
The patent merges learning-based depth estimation (using convolutional neural networks) with geometric optimization (SFM and MVS) to create a hybrid algorithm. The learning-based component provides robust initial depth estimates that are less sensitive to image degradations, while the geometric optimization component refines these estimates to ensure geometric consistency. This combination allows the system to maintain accuracy in challenging conditions where pure SFM/MVS would fail.
Solution Approach 2:
The patent changes the parameter representation by introducing deformation splines that model low-frequency depth variations and high-frequency depth details separately. This parameter transformation allows the optimization process to handle both large-scale depth misalignments and fine-depth details independently, improving convergence and accuracy in dynamic scenes with motion blur and noise.
2Measurement precision
If precise per-frame camera poses are assumed as input, then hybrid algorithms can achieve good results, but the algorithm fails when pose estimation is inaccurate
Solution Approach 1:
The patent makes the camera pose estimation dynamic by jointly optimizing camera poses and depth maps throughout the video sequence. Instead of assuming fixed, pre-estimated poses, the algorithm allows poses to be refined iteratively alongside depth estimation. This dynamic approach enables the system to adapt to pose errors and recover accurate trajectories even in challenging capture conditions.
Solution Approach 2:
The patent implements feedback mechanisms where depth estimation results are used to refine camera pose estimates, which in turn improve depth estimation accuracy. The geometric consistency checks provide feedback loops that identify and correct outlier poses, allowing the algorithm to self-correct and maintain robustness against initial pose estimation errors.
3Reliability
If learning-based algorithms are used for depth estimation, then handling of challenging situations improves, but geometric consistency deteriorates
Solution Approach 1:
The patent introduces geometric optimization as an intermediary step between learning-based depth estimation and final depth map generation. The learning-based algorithm provides robust initial estimates that handle challenging situations well, while the geometric optimization intermediary refines these estimates to ensure they satisfy geometric consistency constraints across multiple views, combining the strengths of both approaches.
Data Source
AI summary
In particular embodiments, a computing system may receive a video comprising a plurality of image frames. The system may generate, for each image frame in the video, an initial depth map using a machine-learning model. The system may compute a misalignment error indicating depth misalignments in initial depth maps using a reprojection technique. The system may generate, for each image frame in the video, an optimized camera pose and a flexible deformation spline associated with the image frame to minimize the misalignment error. The system may generate, for each image frame in the video, a refined depth map by adjusting the initial depth map associated with the frame using the flexible deformation spline associated with the image frame.