Recurrent Decoder for Monocular Depth Estimation
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Solution Overview
Problem
Conventional depth estimation systems face challenges in accurately reconstructing 3D representations from monocular images, particularly in dynamic environments, and rely on costly LIDAR sensors for reliable range data, while also being limited by the need for labeled data and sparse 3D reconstructions.
Innovation Solution
A neural network architecture that incorporates a recurrent decoder with LSTM modules, which upsamples and concatenates features across decoding layers to generate accurate depth estimates, leveraging self-supervised learning and multi-scale information transfer to improve convergence speed and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR sensors are used for depth estimation, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a monocular camera to capture images and a neural network to generate depth maps, creating a software-based copy of the depth estimation function that would otherwise require expensive LIDAR hardware. The recurrent decoder with LSTM modules processes image features to produce accurate depth predictions, replacing physical LIDAR sensors with a computational model trained on image data.
2Device complexity
If conventional depth estimation methods are used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent employs a recurrent decoder with LSTM modules that dynamically process features across multiple time steps and scales. The system adaptively refines depth predictions by sequentially updating estimates based on multi-scale features and temporal information, allowing the model to adjust to varying scene complexities and improve accuracy without requiring complex hardware configurations.
3Measurement precision
If multi-scale features are processed sequentially, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary processing by extracting multi-scale features from the input image at different resolutions before depth estimation. The recurrent decoder pre-processes these features through convolutional layers and LSTM modules to capture temporal and spatial patterns, preparing refined feature representations that accelerate the final depth map generation while maintaining high precision.
Data Source
AI summary
A method for using an artificial neural network associated with an agent to estimate depth, includes receiving, at the artificial neural network, an input image captured via a sensor associated with the agent. The method also includes upsampling, at each decoding layer of a plurality of decoding layers of the artificial neural network, decoded features associated with the input image to a resolution associated with a final output of the artificial neural network. The method further includes concatenating, at each decoding layer, the upsampled decoded features with features obtained at a convolution layer associated with a respective decoding layer. The method still further includes estimating, at a recurrent module of the artificial neural network, a depth of the input image based on receiving the concatenated upsampled decoded features from each decoding layer. The method also includes controlling an action of an agent based on the depth estimate.