Depth Map Completion With Diffusion Modeling Against Overfitting
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Solution Overview
Problem
Training neural network models for depth map completion using supervised learning methods leads to overfitting, resulting in poor depth completion quality.
Innovation Solution
A depth map completion method that aggregates features of a scene image and a sparse depth map, applies noise addition processing, and uses a diffusion model with a diffusion strength parameter to diffuse and complete the aggregated feature, ultimately performing image restoration to generate a dense depth map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning methods are used to train neural network models for depth map completion, then the model can be trained using large amounts of calibrated data, but the model is prone to overfitting resulting in poor depth completion quality
Solution Approach 1:
The patent transforms the depth completion task into a parameter estimation problem within a probabilistic framework. By modeling depth completion as inferring parameters of a generative process rather than direct pixel-wise prediction, the method reduces overfitting and improves generalization to unseen data while maintaining high completion quality
Solution Approach 2:
The patent introduces a probabilistic generative model as an intermediary between the input sparse depth map and the output completed depth map. This intermediary framework enables Bayesian inference, allowing the system to incorporate prior knowledge and uncertainty modeling, thereby improving robustness without sacrificing completion quality
2Reliability
If a diffusion model is used to reduce overfitting risk, then the robustness of the depth completion network is improved, but the complexity of the training process increases
Solution Approach 1:
The patent extracts the core diffusion process from the overall training framework and formulates it as a separate probabilistic generative model. By isolating the diffusion mechanism as a distinct component with its own forward and reverse processes, the method simplifies the overall training complexity while maintaining the robustness benefits of diffusion-based approaches
Data Source
AI summary
This application discloses a depth map completion method and apparatus, a computer device, and a storage medium, and relates to the field of artificial intelligence. The method includes: aggregating features of a scene image and a sparse depth map to obtain an aggregated feature; diffusing and completing the aggregated feature based on a diffusion strength parameter through a depth completion network to obtain a depth completion feature; and performing image restoration based on the depth completion feature to obtain a dense depth map.