Monocular Depth Estimation With Shared-Decoder Uncertainty Metrics
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Solution Overview
Problem
Monocular depth estimation in computer vision is prone to uncertainty due to factors like lighting variability, reflections, and diverse environments, which can lead to erroneous depth estimates, especially in resource-constrained systems, and existing uncertainty quantification methods are computationally expensive.
Innovation Solution
A computationally efficient method using a single-encoder-pass model inference to generate multiple predicted depth maps through shared decoder pathways, allowing for uncertainty quantification by analyzing variances between these maps without requiring multiple model inferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional uncertainty quantification approaches are used, then uncertainty metrics can be obtained, but computational cost becomes excessively high
Solution Approach 1:
The model is divided into shared encoder pathways and separate decoder pathways. Multiple depth predictions are generated by varying only the decoder pathways while reusing the encoder, segmenting the computational workload to reduce overall complexity while maintaining uncertainty quantification capability
Solution Approach 2:
The encoder is designed to serve multiple functions by being shared across different decoder pathways. This universal encoder processes the input image once and feeds features to multiple decoders, enabling efficient generation of multiple depth predictions without redundant encoding computations
2Reliability
If multiple model inferences are performed to quantify uncertainty, then reliable uncertainty metrics are obtained, but processing time increases significantly
Solution Approach 1:
The encoder performs preliminary action by processing the input image once and generating encoded features that are reused by multiple decoders. This preliminary encoding step eliminates the need for repeated full model inferences, significantly reducing processing time while maintaining the ability to generate multiple depth predictions for uncertainty quantification
Solution Approach 2:
Multiple decoder pathways are merged with a single shared encoder, combining the computational operations into a unified architecture. This merging allows multiple depth predictions to be generated in parallel from the same encoded features, reducing total processing time compared to sequential multiple model inferences
3Device complexity
If a single encoder-pass model is used with shared decoders, then computational complexity is reduced, but the ability to generate diverse depth predictions may be limited
Solution Approach 1:
Different decoder pathways are designed with local quality variations, where each decoder has specific characteristics tailored to generate different depth predictions. This allows diversity in depth predictions to emerge from local differences in decoder pathways while sharing the common encoder, maintaining computational efficiency
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for generating an uncertainty metric used in monocular depth prediction. Such techniques may include generating, by an encoder, an encoded feature representation of the input image; generating, by a plurality of depth map prediction pathways, a plurality of outputs corresponding to a plurality of predicted depth maps based on the encoded feature representation; and generating an uncertainty metric indicating an uncertainty of the plurality of predicted depth maps based on one or more variances between the plurality of outputs.