Depth Estimation Training Using Depth Hints and Per-Pixel Loss
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Solution Overview
Problem
Existing depth estimation systems face challenges in accurately determining depth from single-color images due to the high cost and limitations of detection and ranging systems, inaccuracies in stereoscopic image projections, and difficulties in self-supervised training with photometric errors, leading to lower accuracy predictions.
Innovation Solution
A self-supervised training method using depth hints from alternative models, where depth hints are used to improve the training process by calculating contributions to loss based on photometric reconstruction errors, allowing for accurate depth estimation without ground truth data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If self-supervised learning is used to train depth estimation models without ground truth depth maps, then annotation costs and data collection efforts are reduced, but training accuracy and model performance deteriorate due to lack of supervised signals
Solution Approach 1:
The patent introduces an auxiliary network that generates depth hints as intermediary supervision signals during self-supervised training. These depth hints serve as a mediator between the input image and the depth estimation model, providing guidance without requiring expensive ground truth annotations. The auxiliary network processes the input image to produce depth hint maps that guide the main depth estimation model's training, resolving the contradiction between low annotation cost and adequate training accuracy.
2Device complexity
If only input images are used for training without additional depth information, then data collection is simplified, but training effectiveness deteriorates due to insufficient supervisory signals
Solution Approach 1:
The system employs self-service by having the auxiliary network generate its own depth hints from the input images without requiring external ground truth data. The auxiliary network is trained simultaneously with the main depth estimation model, and both networks serve each other - the auxiliary network provides supervision signals to the main model while the main model's predictions help refine the auxiliary network's depth hint generation capability.
3Measurement precision
If ground truth depth maps are required for training, then model accuracy can be maximized, but data collection efforts and annotation costs increase significantly
Solution Approach 1:
The auxiliary network creates a copy or approximation of the ground truth depth information by generating depth hints from the input image alone. Instead of requiring actual ground truth depth maps for training, the system synthesizes surrogate depth information that mimics the supervisory role of true annotations, thereby avoiding the need for expensive annotation efforts while maintaining reasonable model accuracy.
Data Source
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AI summary
A method for training a depth estimation model with depth hints is disclosed. For each image pair: for a first image, a depth prediction is determined by the depth estimation model and a depth hint is obtained; the second image is projected onto the first image once to generate a synthetic frame based on the depth prediction and again to generate a hinted synthetic frame based on the depth hint; a primary loss is calculated with the synthetic frame; a hinted loss is calculated with the hinted synthetic frame; and an overall loss is calculated for the image pair based on a per-pixel determination, wherein if the hinted loss is smaller than the primary loss, then the overall loss includes the primary loss and a supervised depth loss between depth prediction and depth hint. The depth estimation model is trained by minimizing the overall losses for the image pairs.