Two-Stage Depth Estimation with Spherical Warping for 360-Degree Images

The depth estimation process is divided into two distinct stages: a coarse depth estimation stage using monocular cues, and a refined stereo matching stage. This segmentation allows each stage to specialize - the first stage generates an initial depth map with fewer data requirements, while the second stage refines it to high quality using the synthesized views, thus resolving the contradiction between reduced data requirements and maintained depth map quality.

The system performs preliminary depth estimation in the first stage to generate a coarse depth map before proceeding to stereo matching. This preliminary action provides a useful initialization that guides the subsequent refined estimation, allowing the system to achieve high-quality results without requiring extensive training data for the entire process.

The system explicitly incorporates spherical geometry through spherical coordinate transformations and spherical image warping in the synthesized view generation. By using spherical projections rather than planar approximations, the method maintains high depth estimation accuracy for 360-degree images while the differentiable implementation ensures computational efficiency through optimized gradient calculations.

The patent replaces traditional geometric processing methods with differentiable deep learning operations. The spherical warping and view synthesis are implemented as differentiable layers that can be efficiently computed using GPU acceleration, substituting complex mechanical geometric calculations with optimized neural network operations that maintain accuracy while improving computational efficiency.

The system creates synthetic stereo views by warping the monocular 360-degree image using estimated depth information and virtual camera poses. These synthesized views serve as copies of the original scene from different perspectives, enabling stereo matching without requiring physical stereo cameras or expensive LiDAR sensors. This copying approach achieves LiDAR-quality depth maps at a fraction of the cost.

The method replaces expensive, durable sensors like LiDAR with inexpensive computational methods. Instead of relying on costly hardware, the system uses affordable monocular 360-degree cameras combined with computationally intensive but cheap-to-deploy algorithms, achieving similar performance at significantly lower cost.