The paper presents a method for enhancing coarse 5 m digital surface models (DSMs) to 0.5 m resolution by guiding a denoising diffusion process with high‑resolution spectral images. This approach transfers fine visual details—such as crisp outlines and roof structures—from the imagery into the elevation maps, yielding more accurate surface geometry than traditional interpolation or filtering. Experiments on Central European cities show that the resulting DSMs exhibit improved structural detail and overall quality.
The paper proposes a method to enhance coarse 5 m digital surface models (DSMs) to 0.5 m resolution by guiding the super‑resolution process with high‑resolution spectral images. It uses denoising diffusion to transfer image‑visible details, such as crisp outlines and roof structures, into the elevation maps, achieving more accurate surface geometry than traditional interpolation or filtering. Experiments on Central European cities show that the approach yields high‑quality DSMs with improved structural detail.
By Armand Mihai Nicolicioiu, Dominik Narnhofer, Nando Metzger, Daniel Panangian, Ksenia Bittner, Konrad Schindler
arXiv:2606. 02310v1 Announce Type: cross Abstract: Flooding is the most pervasive natural disaster worldwide.
By Yogesh Bhattarai, Vijay Chaudhary, Wai Lim Kim, Sanjib Sharma
arXiv:2607. 01983v1 Announce Type: cross Abstract: Robust 3D object detection under adverse weather remains a critical hurdle for autonomous driving.
By Shuyao Li, Chuanxing Geng, Heyang Sun, Qiang Zhou, Jingjing Gu
LiDAR point clouds acquired in underground environments exhibit severe geometric incompleteness due to occlusions and limited sensor viewpoints, making reliable point cloud completion challenging with...
arXiv:2606. 28039v1 Announce Type: cross Abstract: Demand for high-resolution satellite imagery has increased interest in super-resolution (SR) to bridge the spatial resolution gap between freely available missions such as Sentinel-2 and commercial systems like PlanetScope.
By Dawid Kope\'c, Katarzyna Jab{\l}o\'nska, Wojciech Koz{\l}owski, Maciej Zi\k{e}ba
arXiv:2607. 08711v1 Announce Type: cross Abstract: Accurate 3D terrain maps are essential for emergency response when assessing wildfire hazards.
By Xiao Fu, Yue Hu, Meida Chen, Peter Anthony Beerel, Barath Raghavan
arXiv:2609.24825v1 Announce Type: new
Abstract: LiDAR point clouds acquired in underground environments exhibit severe geometric incompleteness due to occlusions and limited sensor viewpoints, making...
By Daisy Li, Kyle Gao, Quanyun Wu, Boris Jutzi, John S. Zelek, Jonathan Li
Accurate 3D terrain maps are essential for emergency response when assessing wildfire hazards. However, wildfire-prone regions often span vast areas where conventional reconstruction methods underperform.
arXiv:2606. 00094v1 Announce Type: cross Abstract: Image generative models aim to sample data points from the underlying data manifold, a task that requires learning and decoding a dense, low-dimensional, and compact parameterization space.
By Duoduo Xue, Zhiyu Zhu, Junhui Hou
M3GD introduces a multimodal representation that fuses pre‑trained 2D image and 3D LiDAR foundation models for robotic novel view synthesis, avoiding the need for a separate cross‑modal translator. By projecting LiDAR onto the image latent grid and injecting the resulting geometry‑aware packets via a lightweight residual adapter, the method enhances both RGB and depth synthesis on the GrandTour dataset compared to an image‑only baseline. Ablation studies confirm that pixel‑aligned LiDAR content drives the performance gains, and real‑world deployment on a ground robot demonstrates a tunable quality–cost trade‑off.
By Yang Zhou, Jiuhong Xiao, Shizhao Ye, Long Quang, Carlos Nieto-Granda, Giuseppe Loianno
arXiv:2608.29793v1 Announce Type: new
Abstract: Generating realistic 3D city environments from remote sensing data is important for simulation, urban planning, and mixed reality, yet existing point c...
By Xinyu Wang, Muhammad Ibrahim, Atif Mansoor, Ajmal Mian