Search Results (7)

Accurate six-degree-of-freedom (6-DoF) camera pose estimation is essential for augmented reality, robotics navigation, and indoor mapping. Existing pipelines often depend on detailed floorplans, strict Manhattan-world priors, and dense structural annotations, which lead to failures in ambiguous room layouts where multiple rooms appear in a query image and their boundaries may overlap or be partially occluded. We present Render-Rank-Refine, a two-stage framework operating on coarse semantic meshes without requiring textured models or per-scene fine-tuning. First, panoramas rendered from the mesh enable global retrieval of coarse pose hypotheses. Then, perspective views from the top-k candidates are compared to the query via rotation-invariant circular descriptors, which re-ranks the matches before final translation and rotation refinement. Our method increases camera localization accuracy compared to the state-of-the-art SPVLoc baseline by reducing the translation error by 40.4% and the rotation error by 29.7% in ambiguous layouts, as evaluated on the Zillow Indoor Dataset. In terms of inference throughput, our method achieves 25.8–26.4 QPS, (Queries Per Second) which is significantly faster than other recent comparable methods, while maintaining accuracy comparable to or better than the SPVLoc baseline. These results demonstrate robust, near-real-time indoor localization that overcomes structural ambiguities and heavy geometric assumptions. Full article

Urban visual localization is the process of determining the pose (position and attitude) of the imaging sensor (or platform) with the help of existing geo-referenced data. This task is critical and challenging for many applications, such as autonomous navigation, virtual and augmented reality, and robotics, due to the dynamic and complex nature of urban environments that may obstruct Global Navigation Satellite Systems (GNSS) signals. This paper proposes a block-wise matching strategy for urban visual localization by using geo-referenced Google Street View (GSV) panoramas as the database. To determine the pose of the monocular query images collected from a moving vehicle, neighboring GSVs should be found to establish the correspondence through image-wise and block-wise matching. First, each query image is semantically segmented and a template containing all permanent objects is generated. The template is then utilized in conjunction with a template matching approach to identify the corresponding patch from each GSV image within the database. Through the conversion of the query template and corresponding GSV patch into feature vectors, their image-wise similarity is computed pairwise. To ensure reliable matching, the query images are temporally grouped into query blocks, while the GSV images are spatially organized into GSV blocks. By using the previously computed image-wise similarities, we calculate a block-wise similarity for each query block with respect to every GSV block. A query block and its corresponding GSV blocks of top-ranked similarities are then input into a photogrammetric triangulation or structure from motion process to determine the pose of every image in the query block. A total of three datasets, consisting of two public ones and one newly collected on the Purdue campus, are utilized to demonstrate the performance of the proposed method. It is shown it can achieve a meter-level positioning accuracy and is robust to changes in acquisition conditions, such as image resolution, scene complexity, and the time of day. Full article

This Special Issue gathers papers reporting research on various aspects of the use of low-cost photogrammetric and lidar sensors for indoor building reconstruction. It includes contributions presenting improvements in the alignment of mobile mapping systems with and without a prior 3D BIM model, the interpretation of both imagery and lidar data of indoor scenery and finally the reconstruction and enrichment of existing 3D point clouds and meshes with BIM information. Concretely, the publications showcase methods and experiments for the Reconstruction of Indoor Navigation Elements for Point Cloud of Buildings with Occlusions and Openings by Wall Segment Restoration from Indoor Context Labeling, Two-Step Alignment of Mixed Reality Devices to Existing Building Data, Pose Normalization of Indoor Mapping Datasets Partially Compliant with the Manhattan World Assumption, A Robust Rigid Registration Framework of 3D Indoor Scene Point Clouds Based on RGB-D Information, 3D Point Cloud Semantic Augmentation for Instance Segmentation of 360° Panoramas by Deep Learning Techniques and the Symmetry-Based Coarse Registration of Smartphone’s Colorful Point Clouds with CAD Drawings (RegARD) for Low-Cost Digital Twin Buildings. Full article

Recently, the JPEG working group (ISO/IEC JTC1 SC29 WG1) developed an international standard, JPEG 360, that specifies the metadata and functionalities for saving and sharing 360-degree images efficiently to create a more realistic environment in various virtual reality services. We surveyed the metadata formats of existing 360-degree images and compared them to the JPEG 360 metadata format. We found that existing omnidirectional cameras and stitching software packages use formats that are incompatible with the JPEG 360 standard to embed metadata in JPEG image files. This paper proposes an easy-to-use tool for embedding JPEG 360 standard metadata for 360-degree images in JPEG image files using a JPEG-defined box format: the JPEG universal metadata box format. The proposed implementation will help 360-degree cameras and software vendors provide immersive services to users in a standardized manner for various markets, such as entertainment, education, professional training, navigation, and virtual and augmented reality applications. We also propose and develop an economical JPEG 360 standard compatible panoramic image acquisition system from a single PTZ camera with a special-use case of a wide field of view image of a conference or meeting. A remote attendee of the conference/meeting can see the realistic and immersive environment through our PTZ panorama in virtual reality. Full article

The purpose of this paper was to study the influence of cartographic scale and flight design on data acquisition using unmanned aerial systems (UASs) to create augmented reality 3D geovisualization of geosites. The relationship between geographical and cartographic scales, the spatial resolution of UAS-acquired images, along with their relationship with the produced 3D models of geosites, were investigated. Additionally, the lighting of the produced 3D models was examined as a key visual variable in the 3D space. Furthermore, the adaptation of the 360° panoramas as environmental lighting parameters was considered. The geosite selected as a case study was the gorge of the river Voulgaris in the western part of the island of Lesvos, which is located in the northeastern part of the Aegean Sea in Greece. The methodology applied consisted of four pillars: (i) scale-variant flight planning, (ii) data acquisition, (iii) data processing, (iv) AR, 3D geovisualization. Based on the geographic and cartographic scales, the flight design calculates the most appropriate flight parameters (height, speed, and image overlaps) to achieve the desired spatial resolution (3 cm) capable of illustrating all the scale-variant details of the geosite when mapped in 3D. High-resolution oblique aerial images and 360° panoramic aerial images were acquired using scale-variant flight plans. The data were processed using image processing algorithms to produce 3D models and create mosaic panoramas. The 3D geovisualization of the geosite selected was created using the textured 3D model produced from the aerial images. The panoramic images were converted to high-dynamic-range image (HDRI) panoramas and used as a background to the 3D model. The geovisualization was transferred and displayed in the virtual space where the panoramas were used as a light source, thus enlightening the model. Data acquisition and flight planning were crucial scale-variant steps in the 3D geovisualization. These two processes comprised the most important factors in 3D geovisualization creation embedded in the virtual space as they designated the geometry of the 3D model. The use of panoramas as the illumination parameter of an outdoor 3D scene of a geosite contributed significantly to its photorealistic performance into the 3D augmented reality and virtual space. Full article

Due to the appearance of COVID-19 in 2019, person-to-person interactions were drastically reduced. The impact of these restrictions on the economic environment was significant. For example, technical assistance for commissioning or adjusting the parameters of some complex machines/installations had to be postponed. Economic operators became interested in the possibility of remote collaboration, depending on the manufactured products and the performance of the production lines that they owned. This bibliographic research was undertaken to address these needs. The purpose of this review was to analyze the current solutions, approaches, and technologies that workers and specialists can implement to obtain a reliable remote collaboration system. This survey focuses on techniques, devices, and tools that are being used in different contexts to provide remote guidance. We present communication cues and methods being employed, the implemented technological support, and the areas that benefit from remote collaboration. We hope that our effort will be useful to those who develop such systems and people who want to learn about the existence of collaborative solutions, and that it will increase awareness about the applications and the importance of the domain. We are convinced that, with the development of communication systems, the advancement of remote support systems will be a goal for many economic operators. Full article

Improving the hazard-identification skills of construction workers is a vital step towards preventing accidents in the increasingly complex working conditions of construction jobsites. Training the construction workforce to recognize hazards therefore plays a central role in preparing workers to actively understand safety-related risks and make assertive safety decisions. Considering the inadequacies of traditional safety-training methods (e.g., passive lectures, videos, demonstrations), researchers have employed advanced visualization techniques such as virtual reality technologies to enable users to actively improve their hazard-identification skills in a safe and controlled environment. However, current virtual reality techniques sacrifice realism and demand high computational costs to reproduce real environments. Augmented 360-degree panoramas of reality offers an innovative alternative that creates low-cost, simple-to-capture, true-to-reality representations of the actual construction jobsite within which trainees may practice identifying hazards. This proof-of-concept study developed and evaluated a platform using augmented 360-degree panoramas of reality (PARS) for safety-training applications to enhance trainees’ hazard-identification skills for four types of sample hazards. Thirty subjects participated in a usability test that evaluated the PARS training platform and its augmented 360-degree images captured from real construction jobsites. The usability reviews demonstrate that the trainees found the platform and augmentations advantageously to learning hazard identification. The results of this study will foreseeably help researchers in developing engaging training platforms to improve the hazard-identification skills of workers. Full article