Self-Supervised Relocalizer Training for Faster Camera Pose Estimation
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Solution Overview
Problem
Existing structure-from-motion methods for determining camera pose in an environment are slow and inefficient, requiring days to reconstruct scenes with thousands of views, despite being accurate.
Innovation Solution
A self-supervised incremental training approach for a visual relocalizer model that alternates between neural mapping and relocalization, leveraging estimated poses for training without ground truth data, allowing faster and accurate estimation of camera poses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional structure-from-motion methods (e.g., COLMAP) are used, then accurate camera pose estimation is achieved, but the processing time is extremely long (days for thousands of views)
Solution Approach 1:
The patent segments the structure-from-motion process into two distinct phases: (1) an initialization phase using a small subset of images with ground truth poses to train a neural network, and (2) an inference phase where the trained network rapidly estimates poses for remaining images. This segmentation eliminates the need for exhaustive feature matching across all image pairs while maintaining accuracy, reducing processing time from days to hours or minutes.
Solution Approach 2:
The patent performs preliminary action by training a neural network on a small initialization subset of images with known poses before processing the full dataset. This pre-trained model serves as a ready-made predictor that can rapidly estimate poses for remaining images without requiring time-consuming iterative optimization, thus resolving the speed-accuracy tradeoff.
2Manufacturing precision
If traditional structure-from-motion methods are used, then accurate 3D reconstruction is achieved, but the computational complexity and processing steps are numerous
Solution Approach 1:
The patent replaces the mechanical/algorithmic system of iterative feature matching and triangulation with a neural network-based system. The network directly maps image features to 3D scene coordinates and camera poses through learned representations, eliminating the need for complex iterative optimization steps while maintaining reconstruction accuracy.
Solution Approach 2:
The patent creates a neural network model that learns to copy the pose estimation function from a small set of labeled examples. Instead of performing complex geometric computations on the full dataset, the network replicates the pose estimation capability learned from the initialization subset across all images, simplifying the overall process.
Data Source
AI summary
A relocalizer model for an environment is trained using an iterative process. To initialize the relocalizer model, an initial image is registered with its camera pose established as the reference. In each subsequent iteration of training, the relocalizer model is applied to additional images to predict pose estimates for the images. The images and their pose estimates are then leveraged in retraining of the relocalizer model. In general, the training of the relocalizer model entails extracting scene coordinates for pixels of a training image. The scene coordinates are then projected into a projection based on the pose estimate of the training image. A loss is calculated between the projection and the training image. And parameters of the relocalizer model are adjusted to minimize the loss. The iterative training may continue until an end condition is met. The trained relocalizer model is configured to input an image of the environment and to output the camera pose for the image.