Depth Sensor Noise Simulation via Learned Neural Mapping
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Solution Overview
Problem
Current depth sensors suffer from high noise levels in their measurements, with existing noise modeling techniques lacking accuracy and generality, requiring complex physical acquisition process modeling and specific sensor pipeline modeling for each type of depth sensor.
Innovation Solution
A computer-implemented method is developed to learn a noise-adding function using a dataset of noiseless and noisy depth maps, allowing for the simulation of depth sensor noise without needing to mathematically specify the physical noise model, and applicable to any depth sensor, using convolutional neural networks for regression learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional noise modeling techniques are used for depth sensors, then the noise simulation can be performed, but the accuracy and generality are insufficient due to complex physical acquisition process modeling requirements
Solution Approach 1:
The patent creates a virtual depth sensor that copies the noise characteristics of physical depth sensors through learned mappings. Instead of modeling the complex physical acquisition process, the system learns to map from ideal depth maps to noisy depth maps by training on pairs of (ideal_depth_map, noisy_depth_map) data, thereby replicating sensor noise without reproducing the physical sensing mechanism
Solution Approach 2:
The patent replaces the mechanical/physical noise generation process with a data-driven neural network model. The complex physical acquisition process and sensor-specific noise generation mechanisms are substituted by a learned function that directly transforms ideal depth maps into noisy depth maps, eliminating the need to model physical sensor behavior
2Measurement precision
If specific sensor pipeline modeling is performed for each depth sensor type, then the noise characteristics can be captured, but the adaptability to different sensor types is reduced
Solution Approach 1:
The patent creates a universal virtual depth sensor framework that can adapt to different sensor types through learning. The system uses a common architecture that learns sensor-specific noise characteristics during training, allowing the same virtual sensor model to be adapted to various depth sensor types (ToF, structured light, etc.) without requiring dedicated modeling for each sensor category
Solution Approach 2:
The patent enables adaptation to different sensor types by changing the training data parameters rather than the model structure. By training on datasets corresponding to different sensor types and noise characteristics, the system adjusts its learned mapping function to capture the specific noise patterns of each sensor type, maintaining a single versatile model
3Measurement precision
If complex physical acquisition process modeling is used, then the noise model can be specific to sensor type, but the computation time increases significantly
Solution Approach 1:
The patent performs the complex noise modeling work in advance during an offline training phase. The system pre-learns the mapping from ideal to noisy depth maps by training on large datasets, storing the learned parameters and structures. During runtime, the system only needs to apply the pre-trained model, which dramatically reduces computation time while maintaining high noise simulation accuracy
Data Source
AI summary
Described is a computer-implemented method for learning a function configured for reconstructing, for a class of real objects, a 3D modeled object that represents an instance of the class from a depth map of the instance. The method comprises providing a parametric model of the class, creating a database, and learning the function with the database. The method improves the 3D reconstruction of a real object from a depth map.