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Bharat Lohani
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2020 – today
- 2024
- [j8]Moonis Ali
, Bharat Lohani, Markus Hollaus
, Norbert Pfeifer
:
Benchmarking Geometry-Based Leaf-Filtering Algorithms for Tree Volume Estimation Using Terrestrial LiDAR Scanners. Remote. Sens. 16(6): 1021 (2024) - [j7]Abhishek Kuriyal, Vaibhav Kumar
, Bharat Lohani:
pCTFusion: Point Convolution-Transformer Fusion with Semantic Aware Loss for Outdoor LiDAR Point Cloud Segmentation. SN Comput. Sci. 5(3): 272 (2024) - [j6]Aditya
, Bharat Lohani
, Jagannath Aryal
, Stephan Winter
:
Benchmarking Deep Learning Architectures for Urban Vegetation Point Cloud Semantic Segmentation From MLS. IEEE Trans. Geosci. Remote. Sens. 62: 1-14 (2024) - [i3]Vivek Anand, Bharat Lohani, Gaurav Pandey, Rakesh Mishra:
Toward Physics-Aware Deep Learning Architectures for LiDAR Intensity Simulation. CoRR abs/2404.15774 (2024) - 2023
- [i2]Aditya, Bharat Lohani, Jagannath Aryal, Stephan Winter:
Benchmarking Deep Learning Architectures for Urban Vegetation Points Segmentation. CoRR abs/2306.10274 (2023) - [i1]Abhishek Kuriyal, Vaibhav Kumar, Bharat Lohani:
pCTFusion: Point Convolution-Transformer Fusion with Semantic Aware Loss for Outdoor LiDAR Point Cloud Segmentation. CoRR abs/2307.14777 (2023)
2010 – 2019
- 2018
- [j5]Salil Goel
, Allison Kealy
, Bharat Lohani:
Development and Experimental Evaluation of a Low-Cost Cooperative UAV Localization Network Prototype. J. Sens. Actuator Networks 7(4): 42 (2018) - 2015
- [j4]Suddhasheel Ghosh, Bharat Lohani:
Development and comparison of aerial photograph aided visualization pipelines for LiDAR datasets. Int. J. Digit. Earth 8(8): 656-677 (2015) - 2014
- [j3]Salil Goel, Bharat Lohani:
A Motion Correction Technique for Laser Scanning of Moving Objects. IEEE Geosci. Remote. Sens. Lett. 11(1): 225-228 (2014) - 2013
- [j2]Ajay Dashora, Bharat Lohani, Kalyanmoy Deb:
Two-step procedure of optimisation for flight planning problem for airborne LiDAR data acquisition. Int. J. Math. Model. Numer. Optimisation 4(4): 323-350 (2013) - 2012
- [j1]Ajay Dashora, Bharat Lohani:
Compatibility of Sun Position Models and 3-D Topographic Data for Prediction of Shadow Zones. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 5(5): 1458-1463 (2012)
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