Two-Stage Depth Estimation for Equi-Rectangular Stereo Matching
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Solution Overview
Problem
Current methods for depth estimation in 360-degree images face challenges such as low-quality depth maps, high training data requirements, and inefficiencies in handling spherical geometry, leading to suboptimal performance in applications like VR, AR, and autonomous driving.
Innovation Solution
A two-stage depth estimation framework that includes a coarse monocular depth estimation stage using differentiable depth image based rendering and a multi-view stereo matching stage with a spherical warping layer, which generates high-quality omnidirectional depth maps without requiring extensive training resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional depth estimation methods are used for 360-degree images, then the implementation is simpler, but the depth map quality is low and performance is suboptimal
Solution Approach 1:
The patent divides the depth estimation process into two distinct stages: a coarse depth estimation stage using monocular cues and a fine depth refinement stage using multi-view stereo matching. This segmentation allows each stage to focus on specific aspects of depth estimation, improving overall quality while managing computational complexity through progressive refinement.
Solution Approach 2:
The patent performs preliminary coarse depth estimation and view synthesis before conducting the computationally intensive multi-view stereo matching. This preliminary action provides initial depth hypotheses and synthesized views that guide the subsequent refinement stage, reducing the search space and improving final depth map quality.
2Measurement precision
If extensive training data is used to improve depth estimation accuracy, then measurement precision improves, but training resources and time requirements increase
Solution Approach 1:
The system generates its own training data by synthesizing multi-view images from single 360-degree images using the differentiable DIBR pipeline. This self-service approach eliminates the need for extensive external training datasets, allowing the model to learn depth estimation from automatically generated supervision signals while reducing training time and resource requirements.
3Measurement precision
If spherical geometry is handled using traditional methods, then the implementation is straightforward, but performance on 360-degree stereo images is suboptimal
Solution Approach 1:
The patent explicitly models spherical geometry by projecting 360-degree images to a unit sphere and performing stereo matching on spherical surfaces. This approach accounts for the curvature of 360-degree images, improving disparity prediction accuracy by respecting the intrinsic spherical geometry rather than treating images as flat Euclidean planes.
Solution Approach 2:
The patent introduces a spherical warping layer as an intermediary component that transforms images from equirectangular projection to cube map projection. This intermediary transformation facilitates proper spherical stereo matching by aligning epipolar lines and handling spherical geometry, thereby improving disparity accuracy despite the added computational complexity.
4Measurement precision
If expensive LiDAR data is used for training, then depth map quality improves, but cost increases
Solution Approach 1:
The patent creates synthetic copies of depth information by using the differentiable DIBR pipeline to render multi-view images from a single 360-degree image and ground truth depth map. These synthesized views serve as training data, replacing the need for expensive LiDAR scans while maintaining depth map quality through accurate geometric rendering and photometric consistency.
Data Source
AI summary
A system and method is disclosed having an end-to-end two-stage depth estimation deep learning framework that takes one spherical color image and estimate dense spherical depth maps. The contemplated framework may include a view synthesis (stage 1) and a multi-view stereo matching (stage 2). The combination of the two-stage process may provide the advantage of the geometric constraints from stereo matching to improve depth map quality, without the need of additional input data. It is also contemplated that a spherical warping layer may be used to integrate multiple spherical features volumes to one cost volume with uniformly sampled inverse depth for the multi-view spherical stereo matching stage. The two-stage spherical depth estimation system and method may be used in various applications including virtual reality, autonomous driving and robotics.