Search Results for author: Lucio Soibelman

Found 9 papers, 1 papers with code

A New Method of Pixel-level In-situ U-value Measurement for Building Envelopes Based on Infrared Thermography

no code implementations13 Jan 2024 ZiHao Wang, Yu Hou, Lucio Soibelman

The potential energy loss of aging buildings traps building owners in a cycle of underfunding operations and overpaying maintenance costs.

STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset

4 code implementations17 Mar 2022 Meida Chen, Qingyong Hu, Zifan Yu, Hugues Thomas, Andrew Feng, Yu Hou, Kyle McCullough, Fengbo Ren, Lucio Soibelman

Specifically, we introduce a synthetic aerial photogrammetry point clouds generation pipeline that takes full advantage of open geospatial data sources and off-the-shelf commercial packages.

3D Instance Segmentation 3D Semantic Segmentation

Ground material classification for UAV-based photogrammetric 3D data A 2D-3D Hybrid Approach

no code implementations24 Sep 2021 Meida Chen, Andrew Feng, Yu Hou, Kyle McCullough, Pratusha Bhuvana Prasad, Lucio Soibelman

For ground material segmentation, we utilized an existing convolutional neural network architecture (i. e., 3DMV) which was originally designed for segmenting RGB-D sensed indoor data.

Material Classification object-detection +1

3D photogrammetry point cloud segmentation using a model ensembling framework

no code implementations Journal of Computing in Civil Engineering 2020 Meida Chen, Andrew Feng, Kyle McCullough, Pratusha Bhuvana Prasad, Ryan McAlinden, Lucio Soibelman

In this paper, we introduce a model ensembling framework for segmenting a 3D photogrammetry point cloud into top-level terrain elements (i. e., ground, human-made objects, and vegetation).

3D Reconstruction Point Cloud Segmentation

Generating synthetic photogrammetric data for training deep learning based 3D point cloud segmentation models

no code implementations21 Aug 2020 Meida Chen, Andrew Feng, Kyle McCullough, Pratusha Bhuvana Prasad, Ryan McAlinden, Lucio Soibelman

At I/ITSEC 2019, the authors presented a fully-automated workflow to segment 3D photogrammetric point-clouds/meshes and extract object information, including individual tree locations and ground materials (Chen et al., 2019).

Point Cloud Segmentation

Semantic Modeling of Outdoor Scenes for the Creation of Virtual Environments and Simulations

no code implementations Proceedings of the 52nd Hawaii International Conference on System Sciences 2019 Meida Chen, Ryan McAlinden, Ryan Spicer, Lucio Soibelman

Efforts from both academia and industry have adopted photogrammetric techniques to generate visually compelling 3D models for the creation of virtual environments and simulations.

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