Implicit Depth Estimation With Regularized BEV Training
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Solution Overview
Problem
Existing AI models for depth estimation in low-level perception convolution neural networks exhibit high variability and poor generalization due to the lack of sufficient depth ground truth information, leading to unsatisfactory performance in real-world scenarios.
Innovation Solution
Implementing a regularizing loss function based on physics constraints to guide depth estimation, smoothing depth probability distributions through total variation weighting and back-propagation, substituting for depth ground truth during training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If implicit depth estimation is used without sufficient depth ground truth information, then the model can be trained with available data, but the depth estimation exhibits high variability and poor generalization
Solution Approach 1:
The patent introduces a regularizing loss function as an intermediary mechanism that mediates between the available sensor inputs and the depth estimation output. This loss function acts as a virtual supervisor, providing guidance signals that substitute for the missing depth ground truth information, enabling reliable training without requiring extensive labeled depth data.
Solution Approach 2:
The patent modifies the training objective by changing the loss function parameters to include regularization terms that enforce smoothness constraints on depth estimates. By adjusting the weighting parameters in the regularizing loss function, the model learns to produce consistent depth predictions that generalize well to unseen scenarios without requiring depth ground truth for every training example.
2Device complexity
If standard training without regularization is used, then the training process is simpler, but the depth probability distributions exhibit high-frequency narrow spikes and high variability
Solution Approach 1:
The patent applies beforehand cushioning by incorporating regularization terms into the loss function before training begins. These regularization terms pre-establish constraints that prevent the model from learning spurious high-frequency patterns, cushioning against the development of unrealistic depth probability spikes before they can occur during training.
Solution Approach 2:
The regularizing loss function provides continuous feedback during training by computing gradients that guide the model toward smoother depth estimates. This feedback mechanism penalizes unrealistic variations in depth probability distributions, steering the optimization process toward solutions with better generalization properties without requiring complex external validation.
3Measurement precision
If depth ground truth information is extensively collected and used for training, then depth estimation accuracy improves, but the data collection and annotation requirements increase significantly
Solution Approach 1:
The patent enables the model to be self-sufficient by designing a regularizing loss function that allows the model to learn from unlabelled or partially labelled data. The regularization terms provide self-guided learning signals that enable the model to improve its own depth estimation accuracy without requiring extensive external depth ground truth annotations, making the system self-service capable.
Solution Approach 2:
The regularizing loss function serves multiple functions simultaneously: it acts as a training objective, provides regularization to prevent overfitting, guides depth smoothness constraints, and enables learning from unlabelled data. This multi-functional approach allows a single training framework to achieve accurate depth estimation without requiring separate data collection pipelines for different training scenarios.
Data Source
AI summary
An apparatus includes a memory and processing circuitry in communication with the memory. The processing circuitry is configured to train a neural network. Processing circuitry may generate, using a first AI model, a birds-eye-view (BEV) representation from one or more sensor inputs, the BEV representation including BEV features and depth probability distributions. According to such an example, the processing circuitry is also configured to calculate a first loss for the depth probability distributions using a regularizing loss function. The processing circuitry may further be configured to process, using a second AI model, the BEV representation to generate an output and calculate a second loss using the output and ground truth. In at least one example, the processing circuitry is also configured to update parameters of the first AI model based on the first loss and the second loss.