Luhmann, T., Robson, S., Kyle, S., & Boehm, J. (2019). Close-Range Photogrammetry and 3D Imaging. De Gruyter. doi:10.1515/9783110607253

Panella, F., Lipani, A., & Boehm, J. (2022). Semantic segmentation of cracks: Data challenges and architecture. Automation in Construction, 135, 104110. doi:10.1016/j.autcon.2021.104110

Griffiths, D., Boehm, J., & Ritschel, T. (2021). Curiosity-driven 3D Object Detection Without Labels. 2021 International Conference on 3D Vision (3DV). IEEE. doi:10.1109/3dv53792.2021.00062

Kinsler, V., Robson, S., & Boehm, J. (2021). METHOD AND DEVICE FOR DETERMINING NATURE OR EXTENT OF SKIN DISORDER. EP3860437A1.

Sanchez Castillo, E., Griffiths, D., & Boehm, J. (2021). SEMANTIC SEGMENTATION OF TERRESTRIAL LIDAR DATA USING CO-REGISTERED RGB DATA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2021, 223-229. doi:10.5194/isprs-archives-xliii-b2-2021-223-2021

Panella, F., Roecklinger, N., Vojnovic, L., Loo, Y., & Boehm, J. (2020). COST-BENEFIT ANALYSIS of RAIL TUNNEL INSPECTION for PHOTOGRAMMETRY and LASER SCANNING. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 43 (B2), 1137-1144. doi:10.5194/isprs-archives-XLIII-B2-2020-1137-2020

Griffiths, D., Boehm, J., & Ritschel, T. (2020). Finding Your (3D) Center: 3D Object Detection Using a Learned Loss. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12363 LNCS, 70-85. doi:10.1007/978-3-030-58523-5_5

Griffiths, D., & Boehm, J. (2019). A Review on Deep Learning Techniques for 3D Sensed Data Classification. REMOTE SENSING, 11 (12), ARTN 1499. doi:10.3390/rs11121499

Sun, Y., Robson, S., Scott, D., Boehm, J., & Wang, Q. (2019). Automatic sensor orientation using horizontal and vertical line feature constraints. ISPRS Journal of Photogrammetry and Remote Sensing, 150, 172-184. doi:10.1016/j.isprsjprs.2019.02.011

Griffiths, D., & Boehm, J. (2019). Improving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours. ISPRS Journal of Photogrammetry and Remote Sensing, 154, 70-83. doi:10.1016/j.isprsjprs.2019.05.013