Compact building models are demanded by global smart city applications, while high-
definition urban 3D data is increasingly accessible by dint of the advanced reality capture …

We present a neural radiance field method for urban-scale semantic and building-level
instance segmentation from aerial images by lifting noisy 2D labels to 3D. This is a …

Point cloud semantic segmentation (PCSS) shows great potential in generating accurate 3D
semantic maps for digital twin railways. Deep learning-based methods have seen …

Building function identification (BFI) is crucial for urban planning and governance. The
traditional remote sensing approach primarily focuses on extracting the physical features of …

We introduce a novel large-scale scene reconstruction benchmark using the newly
developed 3D representation approach, Gaussian Splatting, on our expansive U-Scene …

Point cloud denoising, which aims to restore high-quality point clouds from noisy input, is an
ingredient in various fields, including 3D mapping, 3D vision, and structured modeling. In …

Point cloud semantic segmentation is an ingredient in understanding real-world scenes.
Most existing approaches perform poorly on scene boundaries and struggle with …

Polygonal mesh reconstruction of a raw point cloud is a valuable topic in the field of
computer graphics and 3D vision. Especially to 3D architectural models, polygonal mesh …

3D multimodal question answering (MQA) plays a crucial role in scene understanding by
enabling intelligent agents to comprehend their surroundings in 3D environments. While …

The semantic segmentation of point clouds is a crucial undertaking in 3D reconstruction and
holds great importance. However, achieving precise semantic segmentation represents a …