DistillPose: Lightweight Camera Localization Using Auxiliary Learning
This work addresses efficient camera localization for applications like robotics and AR, but it is incremental as it builds on existing methods like NN-Net.
The paper tackles the problem of 6DOF camera localization from RGB images by proposing a lightweight retrieval-based pipeline, achieving a 98.87% reduction in parameters and 89.18% faster inference with minimal accuracy loss.
We propose a lightweight retrieval-based pipeline to predict 6DOF camera poses from RGB images. Our pipeline uses a convolutional neural network (CNN) to encode a query image as a feature vector. A nearest neighbor lookup finds the pose-wise nearest database image. A siamese convolutional neural network regresses the relative pose from the nearest neighboring database image to the query image. The relative pose is then applied to the nearest neighboring absolute pose to obtain the query image's final absolute pose prediction. Our model is a distilled version of NN-Net that reduces its parameters by 98.87%, information retrieval feature vector size by 87.5%, and inference time by 89.18% without a significant decrease in localization accuracy.