The fusion of LiDAR and photogrammetry point clouds is a necessary advancement in 3D-modeling, enabling more comprehensive and accurate representations of physical environments. The main contribution of this paper is the development of an innovative fusion system that combines classical algorithms, such as Structure from Motion (SfM), with advanced machine learning techniques, like Coherent Point Drift (CPD) and Feature-Metric Registration (FMR), to improve point cloud alignment and fusion. Experimental results, using a custom dataset of real-world scenes, demonstrate that the hybrid fusion method achieves an average error of less than 5% in the measurements of small reconstructed objects, with large objects showing less than 2% deviation from real sizes. The fusion process significantly improved structural continuity, reducing artifacts like edge misalignments. The k-nearest neighbors (kNN) analysis showed high reconstruction accuracy for the hybrid approach, demonstrating that the hybrid fusion system, particularly when combining machine learning-based refinement with traditional alignment methods, provides a notable advancement in both geometric accuracy and computational efficiency for real-time 3D-modeling applications.

This paper describes in detail the design of the specialized text predictor for patients with Huntington’s disease. The main aim of the specialized text predictor is to improve the text input rate by limiting the phrases that the user can type in. We show that such specialized predictor can significantly improve text input rate compared to a standard general purpose text predictor. Specialized text predictor, however, makes it more difficult for the user to express his own ideas. We further improved the text predictor by using the sematic database to extract synonym, hypernym, and hyponym terms for the words that are not present in the training data of the specialized text predictor. This data can then be used to compute reasonable predictions for words that are originally not known to the text predictor.

Difficulty in communicating is one of the key challenges for people suffering from severe motor and speech disabilities. Often such person can communicate and interact with the environment only using assistive technologies. This paper presents a multifunctional user interface designed to improve communication efficiency and person independence. The main component of this interface is a projection mapping technique used to highlight objects in the environment. Projection mapping makes it possible to create a natural augmented reality information presentation method. The user interface combines a depth sensor and a projector to create camera-projector system. We provide a detailed description of camera-projector system calibration procedure. The described system performs tabletop object detection and automatic projection mapping. Multiple user input modalities have been integrated into the multifunctional user interface. Such system can be adapted to the needs of people with various disabilities.

The authors of this article analyzed the problem of logistic robotics. This paper presents a method for robot navigation in a known environment. The method consists of two steps. The first step is to model the system, assign vector marks to the prominent edges of the virtual environment map, and direct the robot to reach these marks. The second step is to enable the robot to execute a specific task based on the given paths and deal with the local obstacles avoidance independently. The identification of the prominent point, the computation of the vector mark, and optimal path calculation are performed on the computer model using colored Petri nets in the software ‘Centaurus CPN’. The proposed approach was extended to simulate the work of a logistic robot, which has to take boxes and deliver them to certain places in storages. The experimental investigation has shown that the simulated mobile robots with the proposed navigation system were efficiently moving along the planned path. The analysis of the vector tree reveals that it takes 0.389 s to compute and graphically represent it. The occupation of certain places in storages is visualized and shown in experimental graphics.

Reliable integration of LiDAR and photogrammetric point clouds is essential for high-precision 3D mapping, yet systematic evaluations of fusion accuracy under controlled conditions remain limited. This study presents a simulation-based assessment framework for LiDAR–photogrammetry fusion using a neural radiance field (NeRF) representation and iterative closest point (ICP) registration. Experiments were conducted in the CARLA simulator (Town 10), where a drone-mounted multi-sensor platform (RGB, LiDAR, GPS, IMU) generated spatially aligned datasets. Photogrammetric reconstructions were produced using the Nerfstudio–Nerfacto pipeline with varied architectural and sampling configurations to analyze their impact on cross-modal registration. Quantitative evaluation employed Chamfer and cloud-to-cloud (C2C) distances to assess NeRF reconstruction fidelity, and ICP root-mean-square error (RMSE), inlier fitness, and runtime to evaluate registration performance. Results show that tuning NeRF’s hidden dimensions and sampling levels yields up to 25% lower ICP RMSE and faster convergence across object categories. The proposed framework enables reproducible benchmarking of LiDAR–photogrammetry fusion and provides a foundation for extending NeRF-based methods to real-world urban mapping scenarios.

Airborne laser scanning has proven to be an indispensable tool in surveying outdoor areas due to its efficient land coverage and unimpeded access to difficult-to-reach areas. The spatial information in 3D point clouds, the data produced in airborne laser scanning surveys, can be leveraged to glean insights about the target area that would usually be unavailable in 2D-based remote sensing, such as satellite imaging. In forest point clouds, determining the locations and extents of individual trees, such as individual tree segmentation, may lend itself to forest inventory management and hazard prevention applications. However, substantial amounts of annotated and diverse data are necessary to develop classical and Machine Learning algorithms for Individual Tree Segmentation. While 3D point clouds are already notorious for how difficult it is to annotate them, high-altitude airborne laser scans make the task even more difficult due to their lower point density. This makes the structure of individual trees harder to discern, and prior information may be required to determine tree extents. This study proposes an automated approach to extracting individual trees from urban point clouds to construct synthetic datasets, combining point cloud clustering, geometrical features, and crowdsourced geospatial information of tree locations. The proposed method produces a new Individual Tree Segmentation benchmark dataset for airborne laser scanning applications.