Depth-Guided Structure From Motion for Small-Parallax Scenes
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Solution Overview
Problem
Existing Structure from Motion (SfM) techniques are less effective in small-parallax settings, such as movie and TV production, due to insufficient camera movement, leading to inaccurate camera motion and 3D scene geometry estimation, as they rely on large parallax for accurate results.
Innovation Solution
The implementation of depth-guided SfM techniques that utilize a pretrained network for depth-prior estimation to improve geometry-based SfM, combining the strengths of geometry-based and learning-based approaches to handle small-parallax data without requiring additional labeled data, and incorporating depth-priors for robust camera pose and scene geometry estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If geometry-based SfM techniques are used, then measurement precision is improved for large-parallax data, but reliability deteriorates for small-parallax settings
Solution Approach 1:
The patent applies preliminary action by using a pretrained depth estimation network to generate depth maps before the SfM reconstruction process. These depth maps serve as priors that guide the subsequent geometry-based reconstruction, enabling stable convergence even when parallax is insufficient for traditional methods to work reliably.
Solution Approach 2:
The patent combines two different approaches into a hybrid system: learning-based depth estimation (from pretrained networks) and geometry-based SfM (from traditional computer vision). This composite approach leverages the strengths of both methods to achieve reliable reconstruction in small-parallax settings while maintaining measurement precision.
2Reliability
If learning-based approaches are used, then reliability is improved for small-parallax data, but measurement precision deteriorates due to lack of geometric constraints
Solution Approach 1:
The patent uses depth maps from pretrained networks as intermediary representations that bridge the learning-based and geometry-based approaches. These depth priors act as intermediate constraints that guide the geometry-based optimization, enabling the system to handle small-parallax data reliably while maintaining precise measurements through geometric consistency enforcement.
3Measurement precision
If hybrid approaches with learned depth priors are used, then measurement precision is improved, but device complexity increases due to heavy compute and memory resources
Solution Approach 1:
The patent performs depth estimation using pretrained networks as a preliminary step before the main SfM reconstruction. By generating depth maps in advance and using them as priors during optimization, the system achieves high precision without requiring heavy computational resources during the actual reconstruction process, thus reducing overall device complexity requirements.
Data Source
AI summary
Systems, devices, and methods are provided for depth-guided structure from motion. A system may obtain a plurality of image frames from a digital content item that corresponds to a scene and determine, based at least in part on a correspondence search, a set of 2-D keypoints for the plurality of image frames. A depth estimator may be used to determine a plurality of dense depth map for the plurality of image frames. The set of 2-D keypoints and the plurality of dense depth maps may be used to determine a corresponding set of depth priors. Initialization and/or depth-regularized optimization may be performed using the keypoints and depth priors.