A Semantic-Sensitive Approach to Indoor and Outdoor 3D Data Organization

Building model data organization is often programmed to solve a specific problem,resulting in the inability to organize indoor and outdoor 3D scenes in an integrated manner.In this paper,existing building spatial data models are studied,and the characteristics of building information modeling standards(IFC),city geographic modeling language(CityGML),indoor modeling language(IndoorGML),and other models are compared and analyzed.CityGML and IndoorGML models face challenges in satisfying diverse application scenarios and requirements due to limitations in their expression capabilities.It is proposed to combine the semantic information of the model objects to effectively partition and organize the indoor and outdoor spatial 3D model data and to construct the indoor and outdoor data organization mechanism of“chunk-layer-subobject-entrances-area-detail object.”This method is verified by proposing a 3D data organization method for indoor and outdoor space and constructing a 3D visualization system based on it.

Fuzzy-based indoor scene modeling with differentiated examples

Well-designed indoor scenes incorporate interior design knowledge,which has been an essential prior for most indoor scene modeling methods.However,the layout qualities of indoor scene datasets are often uneven,and most existing data-driven methods do not differentiate indoor scene examples in terms of quality.In this work,we aim to explore an approach that leverages datasets with differentiated indoor scene examples for indoor scene modeling.Our solution conducts subjective evaluations on lightweight datasets having various room configurations and furniture layouts,via pairwise comparisons based on fuzzy set theory.We also develop a system to use such examples to guide indoor scene modeling using user-specified objects.Specifically,we focus on object groups associated with certain human activities,and define room features to encode the relations between the position and direction of an object group and the room configuration.To perform indoor scene modeling,given an empty room,our system first assesses it in terms of the user-specified object groups,and then places associated objects in the room guided by the assessment results.A series of experimental results and comparisons to state-of-the-art indoor scene synthesis methods are presented to validate the usefulness and effectiveness of our approach.

Development of an agent-based indoor evacuation model for local fire risks analysis

Evidence suggests that in the event of a fire accident, a certain number of building occupants escape through smoke-filled environments. Consequently, evaluating the corresponding evacuation performance under such life-threatening conditions is important for advancing fire safety analyses. This study aimed to develop a fire-integrated evacuation model to consider the effects of spreading fire hazards (i.e., radiation, temperature, toxic gas, visibility) on evacuees in a room fire evacuation scenario. Furthermore, a novel quantitative approach was introduced to evaluate evacuees’ local fire risks and stress levels according to their egress paths. The escape characteristics at various stages of fire development were studied as well. The results demonstrate that evacuation performance varies considerably depending on the severity of evacuees’ confronted fire hazard conditions, which emphasizes the importance of minimizing the pre-evacuation time in fire evacuation emergencies.

Automatic extraction and reconstruction of a 3D wireframe of an indoor scene from semantic point clouds

Accurate indoor 3D models are essential for building administration and applications in digital city construction and operation.Developing an automatic and accurate method to reconstruct an indoor model with semantics is a challenge in complex indoor environments.Our method focuses on the permanent structure based on a weak Manhattan world assumption,and we propose a pipeline to reconstruct indoor models.First,the proposed method extracts boundary primitives from semantic point clouds,such as floors,walls,ceilings,windows,and doors.The primitives of the building boundary,are aligned to generate the boundaries of the indoor scene,which contains the structure of the horizontal plane and height change in the vertical direction.Then,an optimization algorithm is applied to optimize the geometric relationships among all features based on their categories after the classification process.The heights of feature points are captured and optimized according to their neighborhoods.Finally,a 3D wireframe model of the indoor scene is reconstructed based on the 3D feature information.Experiments on three different datasets demonstrate that the proposed method can be used to effectively reconstruct 3D wireframe models of indoor scenes with high accuracy.

The applications of robust estimation method BaySAC in indoor point cloud processing

Based on Bayesian theory and RANSAC,this paper applies Bayesian Sampling Consensus(BaySAC)method using convergence evaluation of hypothesis models in indoor point cloud processing.We implement a conditional sampling method,BaySAC,to always select the minimum number of required data with the highest inlier probabilities.Because the primitive parameters calculated by the different inlier sets should be convergent,this paper presents a statistical testing algorithm for a candidate model parameter histogram to compute the prior probability of each data point.Moreover,the probability update is implemented using the simplified Bayes’formula.The performances of the BaySAC algorithm with the proposed strategies of the prior probability determination and the RANSAC framework are compared using real data-sets.The experimental results indicate that the more outliers contain the data points,the higher computational efficiency of our proposed algorithm gains compared with RANSAC.The results also indicate that the proposed statistical testing strategy can determine sound prior inlier probability free of the change of hypothesis models.

Indoor scene texturing based on single mobile phone images and 3D model fusion

Realistic texture mapping and coherent up-to-date rendering is one of the most important issues in indoor 3-D modelling.However,existing texturing approaches are usually performed manually during the modelling process,and cannot accommodate changes in indoor environments occurring after the model was created,resulting in outdated and misleading texture rendering.In this study,a structured learning-based texture mapping method is proposed for automatic mapping a single still photo from a mobile phone onto an alreadyconstructed indoor 3-D model.The up-to-date texture is captured using a smart phone,and the indoor structural layout is extracted by incorporating per-pixel segmentation in the FCN algorithm and the line constraints into a structured learning algorithm.This enables real-time texture mapping according to parts of the model,based on the structural layout.Furthermore,the rough camera pose is estimated by pedestrian dead reckoning(PDR)and map information to determine where to map the texture.The experimental results presented in this paper demonstrate that our approach can achieve accurate fusion of 3-D triangular meshes with 2-D single images,achieving low-cost and automatic indoor texture updating.Based on this fusion approach,users can have a better experience in virtual indoor3-D applications.