Occupancy Map Research Articles

Occupancy Map Guided Attributes Artifacts Removal for Video-Based Point Cloud Compression

Point clouds offer realistic 3D representations of objects and scenes at the expense of large data volumes. To represent such data compactly in real-world applications, Video-Based Point Cloud Compression (V-PCC) converts their texture into 2D attributes and occupancy maps before applying lossy video compression. Unfortunately, the coding artifacts introduced in the decoded attribute maps eventually degrade the quality of the reconstructed point cloud, thereby influencing its immersive experience. This article proposes a deep learning-based attribute map enhancement method that fully leverages the occupancy map's guidance. The design philosophy is that the cross-modality guidance from occupancy can be leveraged as critical information to enhance the attribute. Therefore, instead of treating attribute and occupancy as two separate sources of signals, occupancy serves as an indispensable auxiliary, such that the proposed framework explicitly provides the model with abundant clues by conducting local feature modification and global dependencies aggregation. In particular, the proposed framework is compatible with existing V-PCC bitstreams and can be feasibly incorporated into the standardized decoder pipeline. Extensive evaluations show the effectiveness of the proposed framework in attribute enhancement, with equivalently 6.0% Bjontegaard Delta-rate (BD-rate) savings obtained.

SmokeNav: Millimeter‐Wave‐Radar/Inertial Measurement Unit Integrated Positioning and Semantic Mapping in Visually Degraded Environments for First Responders

First responders often face hazardous and life‐threatening situations in environments filled with smoke, posing significant risks to their safety. The existing perception solutions, such as camera or light detection and ranging (LiDAR)‐based methods, are inadequate when faced with visually degraded conditions caused by smoke. In this work, SmokeNav, a novel system that combines data from an inertial sensor and millimeter‐wave (mmWave) radar, is proposed to enhance situational awareness for first responders in smoky environments. SmokeNav utilizes an inertial positioning module that exploits the human motion constraints with a foot‐mounted inertial measurement unit to provide accurate user localization. By integrating this location information with mmWave radar data, it employs a probabilistic occupancy map construction to reconstruct an accurate metric map. To enable semantic understanding of the environment, a DNN‐based semantic segmentation model that incorporates radar reflectivity and employs focal loss to improve performance is introduced. Herein, extensive real‐world experiments in smoky environments is conducted to demonstrate that SmokeNav precisely localizes the user and generates detailed maps with semantic segmentation. In this work, potentials are held for enhancing the safety and effectiveness of first responders in hazardous conditions.

Self-Supervised 3D Semantic Occupancy Prediction from Multi-View 2D Surround Images

An accurate 3D representation of the geometry and semantics of an environment builds the basis for a large variety of downstream tasks and is essential for autonomous driving related tasks such as path planning and obstacle avoidance. The focus of this work is put on 3D semantic occupancy prediction, i.e., the reconstruction of a scene as a voxel grid where each voxel is assigned both an occupancy and a semantic label. We present a Convolutional Neural Network-based method that utilizes multiple color images from a surround-view setup with minimal overlap, together with the associated interior and exterior camera parameters as input, to reconstruct the observed environment as a 3D semantic occupancy map. To account for the ill-posed nature of reconstructing a 3D representation from monocular 2D images, the image information is integrated over time: Under the assumption that the camera setup is moving, images from consecutive time steps are used to form a multi-view stereo setup. In exhaustive experiments, we investigate the challenges presented by dynamic objects and the possibilities of training the proposed method with either 3D or 2D reference data. Latter being motivated by the comparably higher costs of generating and annotating 3D ground truth data. Moreover, we present and investigate a novel self-supervised training scheme that does not require any geometric reference data, but only relies on sparse semantic ground truth. An evaluation on the Occ3D dataset, including a comparison against current state-of-the-art self-supervised methods from the literature, demonstrates the potential of our self-supervised variant.

Leveraging occupancy map to accelerate video-based point cloud compression

Video-based Point Cloud Compression enables point cloud streaming over the internet by converting dynamic 3D point clouds to 2D geometry and attribute videos, which are then compressed using 2D video codecs like H.266/VVC. However, the complex encoding process of H.266/VVC, such as the quadtree with nested multi-type tree (QTMT) partition, greatly hinders the practical application of V-PCC. To address this issue, we propose a fast CU partition method dedicated to V-PCC to accelerate the coding process. Specifically, we classify coding units (CUs) of projected images into three categories based on the occupancy map of a point cloud: unoccupied, partially occupied, and fully occupied. Subsequently, we employ either statistic-based rules or machine-learning models to manage the partition of each category. For unoccupied CUs, we terminate the partition directly; for partially occupied CUs with explicit directions, we selectively skip certain partition candidates; for the remaining CUs (partially occupied CUs with complex directions and fully occupied CUs), we train an edge-driven LightGBM model to predict the partition probability of each partition candidate automatically. Only partitions with high probabilities are retained for further Rate–Distortion (R–D) decisions. Comprehensive experiments demonstrate the superior performance of our proposed method: under the V-PCC common test conditions, our method reduces encoding time by 52% and 44% in geometry and attribute, respectively, while incurring only 0.68% (0.66%) BD-Rate loss in D1 (D2) measurements and 0.79% (luma) BD-Rate loss in attribute, significantly surpassing state-of-the-art works.

New centralities in medium-sized cities: analysis of the subcenters of the Tomba and Cidade Nova districts in Feira de Santana, Bahia

The objective of this article is to analyze the changes in urban centrality in Feira de Santana due to the consolidation of subcenters and to reflect on the alterations triggered by this process in its urban and economic dynamics, through the investigation of the Tomba and Cidade Nova districts. The methodology was based on bibliographic studies to provide a theoretical framework of the center, decentralization, and subcenters; fieldwork including the survey of tertiary establishments in the studied areas, data collection through interviews, and application of qualitative-quantitative questionnaires on the Google Forms platform, as well as the development of land use and occupancy maps and flow maps. The subcenters of Tomba and Cidade Nova can be considered the main expressions of centrality outside the central area of Feira de Santana, due to the diversity of tertiary activities that they offer and the decentralization experienced by the city. The emergence of new centralities affects the role of the Traditional Center, which was once considered the sole polarizing nucleus of the city and which now, due to changes in urban centrality, tends to relatively lose this role.

A New Multimodal Map Building Method Using Multiple Object Tracking and Gaussian Process Regression

Recent advancements in simultaneous localization and mapping (SLAM) have significantly improved the handling of dynamic objects. Traditionally, SLAM systems mitigate the impact of dynamic objects by extracting, matching, and tracking features. However, in real-world scenarios, dynamic object information critically influences decision-making processes in autonomous navigation. To address this, we present a novel approach for incorporating dynamic object information into map representations, providing valuable insights for understanding movement context and estimating collision risks. Our method leverages on-site mobile robots and multiple object tracking (MOT) to gather activation levels. We propose a multimodal map framework that integrates occupancy maps obtained through SLAM with Gaussian process (GP) modeling to quantify the activation levels of dynamic objects. The Gaussian process method utilizes a map-based grid cell algorithm that distinguishes regions with varying activation levels while providing confidence measures. To validate the practical effectiveness of our approach, we also propose a method to calculate additional costs from the generated maps for global path planning. This results in path generation through less congested areas, enabling more informative navigation compared to traditional methods. Our approach is validated using a diverse dataset collected from crowded environments such as a library and public square and is demonstrated to be intuitive and to accurately provide activation levels.

Profiling residential energy vulnerability: Bayesian-based spatial mapping of occupancy and building characteristics

With global trends in electrification, communities highly dependent on electricity are facing increasing challenges, particularly due to more frequent weather events, which lead to significant surges in electricity demand or result in blackouts. However, the impact is disproportionate across space and time, which raises an urgent need to comprehensively assess residential energy vulnerabilities to ensure energy security for households. Existing methodologies, whether large-scale regional assessments or community-based surveys, predominantly rely on static data. This reliance significantly limits their ability to capture the spatial and temporal dynamics of energy vulnerability, which is particularly important in the face of climate change. This study analyses residential energy vulnerability in the dimensions of social vulnerability, building resilience, and energy burden by innovatively linking occupancy patterns with the built environment through a Bayesian-based spatial mapping methodology. We applied this method through a case study in a Philadelphia census tract area identified for its high vulnerability based on FEMA's National Risk Index. By mapping occupancy with residential buildings, we simulated the energy consumption patterns of approximately 900 residential buildings. The simulations utilized DOE single-family energy prototypes, tailored to account for empirical occupancy and building features, to predict hourly energy consumption and assess indoor thermal comfort during power outages on days with extreme temperatures. The findings reveal disparities across different dimensions of energy vulnerability and illustrate the dynamic nature of energy vulnerability that fluctuates with daily activities. The study also highlights areas with low thermal resilience, suggesting a need for retrofitting older buildings to improve energy efficiency. The implications of this research extend beyond the built environment to the socioeconomic aspects of energy use, advocating for adaptive community planning and the development of resilient energy infrastructures. The results serve as a foundation for stakeholders to implement targeted interventions, prioritize retrofitting efforts, and support equitable access to energy resources, thereby enhancing urban sustainability and resilience.

OccTr: A Two-Stage BEV Fusion Network for Temporal Object Detection

Temporal fusion approaches are critical for 3D visual perception tasks in IOV (Internet of Vehicles), but they often rely on intermediate representations without fully utilizing position information from the previous frame’s detection results, which cannot compensate for the lack of depth information in visual data. In this work, we propose a novel framework called OccTr (Occupancy Transformer) that combines two temporal cues, intermediate representation and back-end representation, via occupancy map to enhance temporal fusion in object detection task. OccTr leverages attention mechanisms to perform both intermediate and back-end temporal fusion by incorporating intermediate BEV (bird’s-eye view) features and back-end prediction results of the detector. Our two-stage framework includes occupancy map generation and cross-attention feature fusion. In stage one, the prediction results are converted into occupancy grid map format to generate back-end representation. In stage two, the high-resolution occupancy maps are fused with BEV features using cross-attention layers. This fused temporal cue provides a strong prior for the temporal detection process. Experimental results demonstrate the effectiveness of our method in improving detection performance, achieving an NDS (nuScenes Detection Score) metric score of 37.35% on the nuScenes test set, which is 1.94 points higher than the baseline.

CC-SMC: Chain coding-based segmentation map lossless compression

A segmentation map, either static or dynamic, refers to a two-dimensional picture that may vary with time and indicates the segmentation label per pixel. Both the semantic map and the occupancy map in video-based point cloud compression (V-PCC) belong to the segmentation map we referred to. The semantic map can work for many machine vision tasks like tracking and has been used as a layer of image representation in some image compression methods. The occupancy map constitutes a part of the point cloud coding bitstream. Since segmentation maps are widely used, how to efficiently compress them is of interest. We propose a segmentation map lossless compression scheme namely CC-SMC, exploiting the nature of segmentation maps that usually contain limited colors and sharp edges. Specifically, we design a chain coding-based scheme combined with quadtree-based block partitioning. For intraframe coding, one block is partitioned recursively with a quadtree structure, until the block contains only one color, is smaller than a threshold, or satisfies the defined chain coding condition. We revise the three-orthogonal chain coding method to incorporate contextual information and design effective intraframe prediction methods. For interframe coding, one block may find a reference block; the chain difference between the current and the reference blocks is coded. We implement the proposed scheme and test it on several different kinds of segmentation maps. Compared with advanced lossless image compression techniques, our proposed scheme obtains more than 10% bits reduction as well as more than 20% decoding time-saving. The code is available at https://github.com/Yang-Runyu/CC-SMC.

Robust Detection of Critical Events in the Context of Railway Security Based on Multimodal Sensor Data Fusion.

Effective security surveillance is crucial in the railway sector to prevent security incidents, including vandalism, trespassing, and sabotage. This paper discusses the challenges of maintaining seamless surveillance over extensive railway infrastructure, considering both technological advances and the growing risks posed by terrorist attacks. Based on previous research, this paper discusses the limitations of current surveillance methods, particularly in managing information overload and false alarms that result from integrating multiple sensor technologies. To address these issues, we propose a new fusion model that utilises Probabilistic Occupancy Maps (POMs) and Bayesian fusion techniques. The fusion model is evaluated on a comprehensive dataset comprising three use cases with a total of eight real life critical scenarios. We show that, with this model, the detection accuracy can be increased while simultaneously reducing the false alarms in railway security surveillance systems. This way, our approach aims to enhance situational awareness and reduce false alarms, thereby improving the effectiveness of railway security measures.

Mapping Of Occupation In Quality Management Within National Qualification Framework To Close The Gap Between Education And Industry Needs

In the face of rapid industrial changes and globalization, there is a growing demand for highly skilled professionals in quality management within Indonesia. The Indonesian National Qualifications Framework (IQF) seeks to align vocational education with industry demands by establishing a clear framework of work competency standards. However, discrepancies persist between the competencies taught in vocational programs and those required in the industry, particularly in advanced areas such as data analysis and strategic quality planning. This study undertakes a systematic occupational mapping in the quality management sector to identify these gaps and develop strategies to address them. Utilizing methodologies like the Rapid Assessment Process (RAP), Gall and Borg's educational research methods, and Regional Model Competency Standards (RMCS), the study formulates a comprehensive occupational matrix. This matrix serves as a crucial tool for planning human resources, developing relevant educational and training curricula, and fostering career development aligned with market trends and needs. The research outcomes are aimed at enhancing the alignment of vocational education with industry standards, thereby increasing the employability and effectiveness of graduates in the quality management sector.

VESCL: an open source 2D vessel contouring library.

VESCL (pronounced 'vessel') is a novel vessel contouring library for computer-assisted 2D vessel contouring and segmentation. VESCL facilitates manual vessel segmentation in 2D medical images to generate gold-standard datasets for training, testing, and validating automatic vessel segmentation. VESCL is an open-source C++ library designed for easy integration into medical image processing systems. VESCL provides an intuitive interface for drawing variable-width parametric curves along vessels in 2D images. It includes highly optimized localized filtering to automatically fit drawn curves to the nearest vessel centerline and automatically determine the varying vessel width along each curve. To support a variety of segmentation paradigms, VESCL can export multiple segmentation representations including binary segmentations, occupancy maps, and distance fields. VESCL provides sub-pixel resolution for vessel centerlines and vessel widths. It is optimized to segment small vessels with single- or sub-pixel widths that are visible to the human eye but hard to segment automatically via conventional filters. When tested on neurovascular digital subtraction angiography (DSA), VESCL's intuitive hand-drawn input with automatic curve fitting increased the speed of fully manual segmentation by 22× over conventional methods and by 3× over the best publicly available computer-assisted manual segmentation method. Accuracy was shown to be within the range of inter-operator variability of gold standard manually segmented data from a publicly available dataset of neurovascular DSA images as measured using Dice scores. Preliminary tests showed similar improvements for segmenting DSA of coronary arteries and RGB images of retinal arteries. VESCL is an open-source C++ library for contouring vessels in 2D images which can be used to reduce the tedious, labor-intensive process of manually generating gold-standard segmentations for training, testing, and comparing automatic segmentation methods.

RMSxAI: arginine methylation sites prediction from protein sequences using machine learning algorithms and explainable artificial intelligence

Protein methylation is a vital regulator of many biological processes at the post-translational level, and accurate prediction of protein methylation sites is essential for research and drug discovery. In this paper, we present a new method, namely RMSxAI, to predict the arginine methylation sites from primary sequences using machine learning algorithms and describe the predictions using explainable artificial intelligence (XAI) techniques. Leveraging experimentally validated methylated and unmethylated protein sequences from diverse organisms, we deduced several sequence features, encompassing physicochemical properties, amino acid composition, and evolutionary insights. Our results show that the proposed RMSxAI can predict protein methylation sites with high accuracy, bringing the F1 score up to 0.88 and overall accuracy up to 88.4%. We use various XAI methods to explain the output results. These include key features, partial occupancy maps, and local variation models that provide insight into key features and interactions that lead to predictions. Overall, our approach is relevant to research and drug discovery, and our results demonstrate the potential of machine learning algorithms and XAI methods to provide accurate and meaningful prediction of arginine methylation sites.

‘Finding the play’ – exploring with occupational therapists practice possibilities in the context of Irish schoolyards

Background Research has identified diverse constraints to the adoption of school-based occupational therapy approaches and a lack of attention to addressing the barriers to children’s play opportunities. Critical contextualised research is advocated to inform practice possibilities. Aims/Objectives This inquiry aimed to explore with occupational therapists their existing practices in Irish schoolyards to generate practice possibilities concerned with play, as an issue of occupational justice. Materials and Methods Using the theory of practice architectures, six occupational therapists from diverse sites of practice participated in the first phase of a critical action research process using dialogical focus group and occupational mapping methods. Results Three themes were generated (1) Existing practices as situated (2) (Re)mattering play and practices as occupations and (3) Practice possibilities – ‘Finding the play’ between responsiveness and responsibilities. A further interrelated dimension was how the research methods provided mechanisms of raising consciousness. Conclusions, and Significance Alongside constructing knowledges on existing practices in an Irish context, this inquiry contributes to understandings of practices as socially embedded generative processes of ‘finding the play’, highlighting ethical responsibilities to make visible inequities reproduced in habitual practices and engage in relationships of solidarity to (re)construct alternative shared practices.

Evidence of RNA polymerase III recruitment and transcription at protein-coding gene promoters.

RNA polymerase (Pol) I, II, and III are most commonly described as having distinct roles in synthesizing ribosomal RNA (rRNA), messenger RNA (mRNA), and specific small noncoding (nc)RNAs, respectively. This delineation of transcriptional responsibilities is not definitive, however, as evidenced by instances of Pol II recruitment to genes conventionally transcribed by Pol III, including the co-transcription of RPPH1 - the catalytic RNA component of RNase P. A comprehensive understanding of the interplay between RNA polymerase complexes remains lacking, however, due to limited comparative analyses for all three enzymes. To address this gap, we applied a uniform framework for quantifying global Pol I, II, and III occupancies that integrates currently available human RNA polymerase ChIP-seq datasets. Occupancy maps are combined with a comprehensive multi-class promoter set that includes protein-coding genes, noncoding genes, and repetitive elements. While our genomic survey appropriately identifies recruitment of Pol I, II, and III to canonical target genes, we unexpectedly discover widespread recruitment of the Pol III machinery to promoters of specific protein-coding genes, supported by colocalization patterns observed for several Pol III-specific subunits. We show that Pol III-occupied Pol II promoters are enriched for small, nascent RNA reads terminating in a run of 4 Ts, a unique hallmark of Pol III transcription termination and evidence of active Pol III activity at these sites. Pol III disruption differentially modulates the expression of Pol III-occupied coding genes, which are functionally enriched for ribosomal proteins and genes broadly linked to unfavorable outcomes in cancer. Our map also identifies additional, currently unannotated genomic elements occupied by Pol III with clear signatures of nascent RNA species that are sensitive to disruption of La (SSB) - a Pol III-related RNA chaperone protein. These findings reshape our current understanding of the interplay between Pols II and III and identify potentially novel small ncRNAs with broad implications for gene regulatory paradigms and RNA biology.

CollaborativeBEV: Collaborative bird eye view for reconstructing crowded environment

Constructing a virtual world for the Metaverse based on real-world data is crucial, yet creating virtual environments for crowded scenes poses challenges in accurately tracking individuals using egocentric wearable cameras due to occlusions caused by crowded pedestrians. To address this, we propose a collaborative perception strategy that leverages multiple agents equipped with multi-view cameras to construct an occupancy map for a crowded environment. To fuse the multi-view perceptions of multiple agents, we propose a Collaborative Bird Eye View fusion network, called CollaborativeBEV (C-BEV), in which, we leverage a depth-based BEV network as a feature extractor, and propose a feature enhancement module to improve perception fusion in overlapping area. A designed loss function is introduced to address data imbalance during training, and a BEV enhancement strategy is proposed to augment the sample pool for training the BEV decoder. Experiment on the Sean2.0 dataset demonstrates that our C-BEV method performs better than the baseline method in terms of a 5.3% IoU increase. Our code will be released on github. https://github.com/RYaNzzZ1/CollaborativeBEV.

Design and integration of a radiation detector module for robot operating system (ROS)

In this paper, we present a radiation detector module that can be seamlessly integrated with Robot Operating System (ROS) to enable robots to perform radiation measurements in hazardous environments. The module is designed with a detector PCB and connectors that are compatible with an Arduino shield. The Arduino firmware is programmed with a counter-timer algorithm and publishes data to the ROS environment, allowing for easy visualization of the data in a 2D occupancy map. Our experimental results demonstrate the module’s effectiveness in inspecting and reconstructing the robot’s path during operations. This paper provides a valuable contribution to the field of robotics by enabling robots to perform radiation measurements safely and accurately in dangerous environments.

Study on Mapping of Qualification, Occupation, and Competence in Human Resources Management to Improve Link and Match Between Industry and Education Program

This study aims to map qualifications, jobs, and competencies in the field of Human Resource Management (HRM) to improve compatibility between industry and educational programs. Using the Rapid Assessment Process method, this study identifies job structures that can be described in accordance with regional competency standards and national qualification frameworks. The results showed that there is a match between industry needs and educational outcomes in the field of HRM. The research successfully formulated an interface model between the structure of the job map, skill sets in the educational operational curriculum and certification schemes, allowing the identification of links and fits between industry needs and educational outcomes. Recommendations for advanced research include increased collaboration between educational institutions and industry in curriculum development, using competency-based approaches, and implementing standardization and accreditation for qualifications and competencies in various industries. This research presents important contributions in mapping qualifications, occupations, and competencies in HR relevant to current and future industry needs.

Graph-Based Prediction and Planning Policy Network (GP3Net) for Scalable Self-Driving in Dynamic Environments Using Deep Reinforcement Learning

Recent advancements in motion planning for Autonomous Vehicles (AVs) show great promise in using expert driver behaviors in non-stationary driving environments. However, learning only through expert drivers needs more generalizability to recover from domain shifts and near-failure scenarios due to the dynamic behavior of traffic participants and weather conditions. A deep Graph-based Prediction and Planning Policy Network (GP3Net) framework is proposed for non-stationary environments that encodes the interactions between traffic participants with contextual information and provides a decision for safe maneuver for AV. A spatio-temporal graph models the interactions between traffic participants for predicting the future trajectories of those participants. The predicted trajectories are utilized to generate a future occupancy map around the AV with uncertainties embedded to anticipate the evolving non-stationary driving environments. Then the contextual information and future occupancy maps are input to the policy network of the GP3Net framework and trained using Proximal Policy Optimization (PPO) algorithm. The proposed GP3Net performance is evaluated on standard CARLA benchmarking scenarios with domain shifts of traffic patterns (urban, highway, and mixed). The results show that the GP3Net outperforms previous state-of-the-art imitation learning-based planning models for different towns. Further, in unseen new weather conditions, GP3Net completes the desired route with fewer traffic infractions. Finally, the results emphasize the advantage of including the prediction module to enhance safety measures in non-stationary environments.

Occupancy map-based low complexity motion prediction for video-based point cloud compression

This paper proposes an occupancy map-based low complexity motion prediction method for video-based point cloud compression (V-PCC). Firstly, we propose to utilize the occupancy map, direction gradient, and regional dispersion to divide the attribute maps into static, complex, and common blocks. Then, we propose an early termination method for static blocks, an adaptive motion search range method for complex blocks, and an early inter prediction mode decision algorithm for affine motion regions in common blocks. Experiment results show that, in comparison to the test model category2 (TMC2) v15.0, called the anchor method, the average bitrate savings of Y, U, and V components of the proposed method achieve 24.27%, 32.64%, and 31.23% on 8i voxelized full bodies version 2 (8iVFBv2) dataset, respectively. Further, the time savings is 41.97% for attribute maps. Similarly, the proposed method also achieves consistent performance on Microsoft voxelized upper bodies (MVUB) dataset.