Depth Estimation Using 2D Coordinate and Depth Distribution Channels
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Solution Overview
Problem
Conventional systems lack inherent awareness of locations within an image during depth estimation from 2D camera images, leading to scale ambiguity and inaccurate depth predictions.
Innovation Solution
Generate and utilize depth distribution maps and 2D coordinate channels as additional inputs to machine learning models, aligning 2D image data with 3D space to provide absolute scale and spatial awareness for improved depth estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use only 2D camera images for depth estimation, then the system complexity remains low, but depth prediction accuracy deteriorates due to scale ambiguity
Solution Approach 1:
The system performs preliminary actions by generating depth distribution maps and coordinate channels before feeding data to the machine learning model. These preprocessed channels encode spatial relationships and depth priors, enabling the model to make more accurate depth predictions without increasing fundamental system complexity
Solution Approach 2:
The invention transitions from 2D image data to 3D depth estimation by introducing additional dimensional information through depth distribution maps and coordinate channels. This dimensional enhancement provides the model with spatial awareness and scale information that was previously unavailable in 2D images alone
2Measurement precision
If additional channels (depth distribution map, coordinate channels) are added to the machine learning model input, then depth estimation accuracy improves, but processing complexity increases
Solution Approach 1:
The input data is segmented into multiple functional channels: the original image channels, the depth distribution map channel, and coordinate channels. Each channel serves a specific purpose and processes information independently, allowing the model to handle complex spatial relationships without overwhelming processing complexity
Solution Approach 2:
The additional channels serve multiple functions simultaneously: they provide spatial location information, encode depth priors, establish coordinate systems, and guide the model's attention. This multi-functionality reduces the need for separate processing mechanisms, thereby limiting the increase in processing complexity
Data Source
AI summary
In various examples, depth predictions obtained using machine learning models may be improved by leveraging relationships associated with two-dimensional (2D) images and three-dimensional (3D) environments. For instance, systems and methods are disclosed that may generate and use a depth distribution map as an additional input channel to a machine learning model. This depth distribution channel may represent average depth values for respective pixels of 2D images generated using a sensor. Additionally, or alternatively, the disclosed systems and methods may generate and use a 2D coordinate channel (e.g., Y coordinate channel) that is aligned with depth in 3D space. For example, the 2D coordinate channel may include points having values that increase in magnitude from a bottom portion of a frame to a top portion of the frame. One or more of these channels may then be applied to the machine learning model to improve depth predictions.