Monocular Depth Estimation via Rigid-Motion Embeddings
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Solution Overview
Problem
Existing systems face challenges in accurately determining depth from monocular images, particularly when dynamic objects are present, as they often require additional object-level supervision, which increases complexity and training requirements.
Innovation Solution
A novel network architecture is implemented, which includes a depth pipeline with a pose model, a depth model, and a motion model. The motion model predicts rigid-motion embeddings without explicit supervision, allowing for the identification of dynamic objects and improved depth estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object detectors are integrated to handle dynamic objects, then depth estimation accuracy is improved, but device complexity and training requirements increase
Solution Approach 1:
The patent combines the dynamic object detection capability and depth estimation functionality into a single integrated model. The model simultaneously predicts depth maps and identifies dynamic objects by analyzing motion patterns, eliminating the need for separate object detectors and reducing overall system complexity while maintaining depth estimation accuracy
Solution Approach 2:
The depth estimation model is designed to perform multiple functions: it estimates depth from monocular images and simultaneously detects dynamic objects by analyzing motion patterns in the scene. This multi-functional approach allows a single model to handle both tasks that would traditionally require separate specialized components
2Measurement precision
If object detectors are integrated to handle dynamic objects, then depth estimation accuracy is improved, but training requirements increase
Solution Approach 1:
The model performs self-supervised learning by automatically identifying dynamic objects through motion pattern analysis and using these identifications to generate its own training signals. The system creates its own supervision labels from the input video sequences without requiring external annotated data, enabling efficient training while handling dynamic objects accurately
3Measurement precision
If additional object-level supervision is used, then depth estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent introduces motion embeddings as an intermediary representation that captures dynamic object information from video sequences. These motion embeddings serve as a bridge between the input video and the depth estimation output, enabling the model to handle dynamic objects without requiring complex object-level supervision or separate detection modules
Data Source
AI summary
System, methods, and other embodiments described herein relate to training a depth model for monocular depth estimation using photometric loss masks derived from motion estimates of dynamic objects. In one embodiment, a method includes generating depth maps from images of an environment. The method includes determining motion of points within the depth maps. The method includes associating the points between the depth maps to identify an object according to a correlation of the motion for a first cluster of the points with a second cluster of the points. The method includes providing the depth maps and the object as an electronic output.