Enhancing Polygonal Building Segmentation via Oriented Corners

The growing demand for high-resolution maps across various applications has underscored the necessity of accurately segmenting building vectors from overhead imagery. However, current deep neural networks often produce raster data outputs, leading to the need for extensive post-processing that compromises the fidelity, regularity, and simplicity of building representations. In response, this paper introduces a novel deep convolutional neural network named OriCornerNet, which directly extracts delineated building polygons from input images. Specifically, our approach involves a deep model that predicts building footprint masks, corners, and orientation vectors that indicate directions toward adjacent corners. These predictions are then used to reconstruct an initial polygon, followed by iterative refinement using a graph convolutional network that leverages semantic and geometric features. Our method inherently generates simplified polygons by initializing the refinement process with predicted corners. Also, including geometric information from oriented corners contributes to producing more regular and accurate results. Performance evaluations conducted on SpaceNet Vegas and CrowdAI-small datasets demonstrate the competitive efficacy of our approach compared to the state-of-the-art in building segmentation from overhead imagery.

HDPV-SLAM: Hybrid Depth-augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera

This paper proposes a novel visual simultaneous localization and mapping (SLAM), called Hybrid Depth-augmented Panoramic Visual SLAM (HDPV-SLAM), generating accurate and metrically scaled vehicle trajectories using a panoramic camera and a titled multi-beam LiDAR scanner. RGB-D SLAM served as the design foundation for HDPV-SLAM, adding depth information to visual features. It seeks to overcome the two problems that limit the performance of RGB-D SLAM systems. The first barrier is the sparseness of LiDAR depth, which makes it challenging to connect it with visual features extracted from the RGB image. We address this issue by proposing a depth estimation module for iteratively densifying sparse LiDAR depth based on deep learning (DL). The second issue relates to the challenges in the depth association caused by a significant deficiency of horizontal overlapping coverage between the panoramic camera and the tilted LiDAR sensor. To overcome this difficulty, we present a hybrid depth association module that optimally combines depth information estimated by two independent procedures, feature triangulation and depth estimation. This hybrid depth association module intends to maximize the use of more accurate depth information between the triangulated depth with visual features tracked and the DL-based corrected depth during a phase of feature tracking. We assessed HDPV-SLAM's performance using the 18.95 km-long York University and Teledyne Optech (YUTO) MMS dataset. Experimental results demonstrate that the proposed two modules significantly contribute to HDPV-SLAM's performance, which outperforms the state-of-the-art (SOTA) SLAM systems.