Monocular Depth Estimation via Supervised Learning

The patent replaces complex mechanical/optical systems (binocular stereo vision, laser scanners) with a computational approach using monocular images processed through supervised learning algorithms. The depth estimation is achieved by training a neural network on labeled depth data, substituting physical depth-sensing hardware with software-based analysis of standard images.

The patent creates a computational model that learns to replicate the depth estimation capability of expensive hardware systems. By training on datasets containing both monocular images and corresponding depth maps, the system creates a virtual copy of depth-sensing functionality that can be deployed without physical depth sensors.

The patent performs preliminary training action by collecting and processing large datasets of monocular images with corresponding depth maps before actual depth estimation. This pre-training phase enables the system to learn from diverse real-world conditions including various lighting, textures, and depth configurations, making the model robust without requiring restrictive assumptions during operation.

The patent transforms the depth estimation problem from a deterministic algorithmic approach to a probabilistic learning approach. By changing from fixed algorithmic parameters to learned model parameters through supervised training, the system can handle noise and variability in real images while maintaining computational efficiency.