This report summarizes the results of the three-year research project Imaging photoncounting laser sensors that has been ongoing from 2020 to 2022. This work has been funded by the Swedish Armed Forces R&D programme for Sensors and low observables (FoT SoS, AT.9220422). The project has studied the utility of photoncounting lidar systems for 3D imaging, including how they can be implemented on mobile platforms, and what signal processing methods for 3D data than can add capability to military systems. In addition to results, the report also describes other activities in the field of imaging laser sensors, collaboration with other research organizations and the dissemination of knowledge to the Swedish Armed Forces, FMV and other stakeholders. Data from imaging photon-counting laser sensors enables the detection and identification of partially hidden objects behind vegetation or other cover. This is because the sensor provides higher range resolution than other types of lidar systems, and is thus better at separating objects from foliage. At daytime, the sensor has a disadvantage because the sensitivity must be lowered to avoid the sensor being saturated by background light from the sun. Therefore, performance is best during nighttime, but the technology is still useful during daytime. Measurements to analyse the possibilities with photon-counting 3D imaging have been carried out in different environments. When collecting data with a photon-counting sensor on a moving platform, the position and measuring direction of the sensor must be known at any time with higher accuracy than the resolution of the sensor. To enable the use of the sensor even without supporting gyro sensors that are larger and more expensive than the sensor itself, drift in the motion model can be corrected with the measured 3D data for cases where the sensor has scanned repeatedly over the same object. Because the field of view of the sensor is small, the requirements of stabilisation and measuring the position and pointing direction of the sensor are still demanding despite the evaluated correction. A 3D sensor with high resolution will produce large amounts of data that need to be processed and analysed automatically before a result can be presented to an operator. Our research results show that methods based on machine learning can deliver good analysis results on data for military applications like reconnaissance and surveillence. Methods for dividing data into the classes ground, vegetation, and vehicle have been evaluated in data from a compact scanning lidar mounted on a small UAV and methods for recognizing people near a forest edge have been evaluated in data from a vehicle mounted photon-counting lidar. The methods provide good performance for different types of lidar data. Another result is that change detection is an important tool for detecting differences in point clouds measured at different times. Repeated measurements can be used to detect people moving within a forest edge or behind cover, and measurements at different times provide the ability to detect results of activity. In general, automatic methods for analysing point clouds can be used for tasks such as situation awareness, detection and recognition of targets, change detection, terrain analysis, and self-positioning.