A-SCoRe: Attention-based Scene Coordinate Regression for wide-ranging scenarios

Visual localization is considered to be one of the crucial parts in many robotic and vision systems. While state-of-the art methods that relies on feature matching have proven to be accurate for visual localization, its requirements for storage and compute are burdens. Scene coordinate regression (SCR) is an alternative approach that remove the barrier for storage by learning to map 2D pixels to 3D scene coordinates. Most popular SCR use Convolutional Neural Network (CNN) to extract 2D descriptor, which we would argue that it miss the spatial relationship between pixels. Inspired by the success of vision transformer architecture, we present a new SCR architecture, called A-ScoRe, an Attention-based model which leverage attention on descriptor map level to produce meaningful and high-semantic 2D descriptors. Since the operation is performed on descriptor map, our model can work with multiple data modality whether it is a dense or sparse from depth-map, SLAM to Structure-from-Motion (SfM). This versatility allows A-SCoRe to operate in different kind of environments, conditions and achieve the level of flexibility that is important for mobile robots. Results show our methods achieve comparable performance with State-of-the-art methods on multiple benchmark while being light-weighted and much more flexible. Code and pre-trained models are public in our repository: https://github.com/ais-lab/A-SCoRe.

Improved 3D Point-Line Mapping Regression for Camera Relocalization

In this paper, we present a new approach for improving 3D point and line mapping regression for camera re-localization. Previous methods typically rely on feature matching (FM) with stored descriptors or use a single network to encode both points and lines. While FM-based methods perform well in large-scale environments, they become computationally expensive with a growing number of mapping points and lines. Conversely, approaches that learn to encode mapping features within a single network reduce memory footprint but are prone to overfitting, as they may capture unnecessary correlations between points and lines. We propose that these features should be learned independently, each with a distinct focus, to achieve optimal accuracy. To this end, we introduce a new architecture that learns to prioritize each feature independently before combining them for localization. Experimental results demonstrate that our approach significantly enhances the 3D map point and line regression performance for camera re-localization. The implementation of our method will be publicly available at: https://github.com/ais-lab/pl2map/.