159 citations · 443 across the 7 of their papers we have counts for
5 papers · 1 filter
DDL-MVS: Depth Discontinuity Learning for MVS Networks
Nail Ibrahimli, Hugo Ledoux, Julian Kooij +1
Traditional MVS methods have good accuracy but struggle with completeness, while recently developed learning-based multi-view stereo (MVS) techniques have improved completeness exc…
PSSNet: Planarity-sensible Semantic Segmentation of Large-scale Urban Meshes
Weixiao Gao, Liangliang Nan, Bas Boom +1
We introduce a novel deep learning-based framework to interpret 3D urban scenes represented as textured meshes. Based on the observation that object boundaries typically align with…
Reconstructing Compact Building Models from Point Clouds Using Deep Implicit Fields
Zhaiyu Chen, Hugo Ledoux, Seyran Khademi +1
While three-dimensional (3D) building models play an increasingly pivotal role in many real-world applications, obtaining a compact representation of buildings remains an open prob…
SUM: A Benchmark Dataset of Semantic Urban Meshes
Weixiao Gao, Liangliang Nan, Bas Boom +1
Recent developments in data acquisition technology allow us to collect 3D texture meshes quickly. Those can help us understand and analyse the urban environment, and as a consequen…
The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo
Michael Kölle, Dominik Laupheimer, Stefan Schmohl +4
Automated semantic segmentation and object detection are of great importance in geospatial data analysis. However, supervised machine learning systems such as convolutional neural…