Multi-Flash Stereo Camera for Limited-View Scene Reconstruction
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Solution Overview
Problem
Current methods for synthesizing accurate geometry and photo-realistic appearance of small scenes in robotics are hindered by limited robot motion and scene clutter, leading to poor quality estimates.
Innovation Solution
A multi-flash stereo camera system that captures multiple views and lighting conditions, combined with a neural network trained using depth values, to reconstruct scenes by incorporating a neural network with intrinsic and appearance networks for joint optimization of geometry and appearance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current estimation techniques are applied to robotics with limited robot motion and scene clutter, then the system operates with constrained viewpoints, but the quality of geometry and appearance estimates deteriorates
Solution Approach 1:
The system performs preliminary action by capturing multiple images from different viewpoints and lighting conditions before the actual reconstruction task. This pre-capture of diverse data enables the neural network to achieve high-quality geometry and appearance estimates even when the robot's motion range is limited during operation.
Solution Approach 2:
The invention adds another dimension by incorporating multiple lighting conditions alongside viewpoint variations. The multi-flash stereo camera captures images under different illuminations, creating a multi-dimensional dataset that enriches the training data for the neural network, thereby improving estimation quality without requiring extended robot motion.
2Measurement precision
If multiple images with different lighting conditions are captured to improve scene reconstruction quality, then the training data quality improves, but the complexity of the capture system increases
Solution Approach 1:
The invention merges multiple functions into a single integrated system. The multi-flash stereo camera combines stereo imaging capabilities with multiple controllable light sources into one device, enabling simultaneous capture of geometric and appearance information under varied lighting conditions without requiring separate capture systems.
Solution Approach 2:
The capture system achieves multi-functionality by using the same camera hardware to capture both geometric depth information and appearance information under multiple lighting conditions. This universal approach allows a single system to perform what would traditionally require multiple specialized devices.
3Productivity
If dense metric depth is used to provide initial geometry estimates for reconstruction, then the convergence speed improves, but the requirement for accurate depth measurement increases
Solution Approach 1:
The system implements feedback by using the captured images and initial depth estimates to train the neural network, which then refines both geometry and appearance. The multi-view and multi-lighting information provides continuous feedback that allows the network to correct depth estimation errors and improve overall reconstruction accuracy iteratively.
Data Source
AI summary
A method may include receiving a plurality of pairs of images of a scene captured by a camera, receiving a pose of the camera when each of the pairs of images of the scene were captured, determining depth values of the scene for each pair of images, and training a neural network to receive a pose of a camera with respect to the scene, and output a geometry of the scene and an appearance of the scene with respect to the pose. The plurality of pairs of images, the poses of the camera, and the depth values may be used as training data to train the neural network. The neural network may include a first component that receives the pose as input, and outputs the geometry of the scene and an embedding, and a second component that receives the embedding as input, and outputs the appearance of the scene.