The paper introduces an adaptive region‑dividing strategy that projects a 3D point cloud onto a bird’s‑eye‑view plane to detect building regions, then back‑projects bounding boxes to create structure‑aligned training blocks for unified scene‑level evaluation. It also proposes a fine‑grained classification model using a point transformer classifier and a spatially‑supervised contrastive loss to improve inter‑class discriminability, addressing class imbalance with a weighted cross‑entropy. Experiments on UrbanBIS and STPLS3D datasets show the method outperforms state‑of‑the‑art approaches in both building instance segmentation and fine‑grained classification.
By Weiyuan Zhang, Qi Zhang, Hui Huang
arXiv:2512. 09062v2 Announce Type: replace-cross Abstract: Accurate 3D scene interpretation in active construction sites is essential for progress monitoring, safety assessment, and digital twin development.
By Seongyong Kim, Yong Kwon Cho
arXiv:2611.06180v1 Announce Type: new
Abstract: We present the Rohbau3D-Semantics dataset, which adds dense semantic and instance-level labels to laser scans of the previously released Rohbau3D domai...
By Lukas Rauch, Thomas Braml
The paper introduces a measurable semantic 3D Gaussian Splatting (3DGS) framework that uses region and depth supervision to improve building inspection from multi‑view imagery. Mask‑based region supervision focuses optimization on target areas, while point‑wise depth supervision aligns Gaussian centers with physical surfaces derived from multi‑view stereo point clouds. The resulting semantic Gaussian field enables component‑level inspection and is deployed in the AEC‑SplatTwin platform, achieving significant reductions in depth error and high semantic segmentation accuracy on UAV‑inspected and smartphone‑captured datasets.
By Yi Zhang, Tian Tan, Ray Kai Leung Su
arXiv:2608.29992v1 Announce Type: new
Abstract: This paper presents an end-to-end, agent-driven pipeline for the LoD3 reconstruction of facade openings in 3D city models, producing directly usable Ci...
By Elmehdi Kanna, Lukas Arzoumanidis, Huynh Duc An Son Nguyen, Youness Dehbi
The paper presents a two‑stage approach to street‑panorama semantic segmentation using Vision Transformer ensembles. In the first stage, nine pretrained models are compared under equal compute budgets, and the best two are trained with three random seeds, achieving 60.95% mIoU on validation and 57.08% on the hidden test leaderboard. The second stage explores augmentations, larger inputs, and additional architectures, culminating in an ensemble of four models that scores 64.53% mIoU on validation and 61.54% mIoU (71.93% F1) on the hidden test leaderboard, while the study documents all runs, costs, and releases the code and models.
By Yunus Serhat B{\i}\c{c}ak\c{c}{\i}