Summary for CIFE Seed Proposals for Academic Year 2016-17 ... · Type of innovation Breakthrough Abstract (up to 150 words) Understanding a building’s state in space and through

Summary for CIFE Seed Proposals for Academic Year 2016-17

Proposal number:

Proposal title: Automated Spatiotemporal Semantic Understanding of Buildings in Construction and Use Phases

Principal investigators and departments:

Martin Fischer, Civil and Environmental Engineering Department Silvio Savarese, Computer Science Department

Research staff: Iro Armeni, PhD candidate

Total funds requested: $ 50,155

Project objectives addressed by proposal

Buildable, Operable (If extended: +Usable)

Expected time horizon 2 to 5 years

Type of innovation Breakthrough

Abstract (up to 150 words)

Understanding a building’s state in space and through time is essential for many processes in the Architecture, Engineering, Construction and Facilities Management (AEC-FM) domain. Currently laser scans provide the most accurate information about space and when collected repeatedly they capture its transformation over time as well. However, point clouds lack semantic information about the depicted area; there is no knowledge about the type of building elements present (e.g. walls, columns, etc.), their location or the in-between relationships. As a result, in the context of building structures, spatial and temporal changes need to be inferred by humans, with significant time allocation. We propose to develop a method based on Computer Vision and Machine Learning that automates the creation of spatial meaning through time with the generation of a spatiotemporal 3D semantic model of the facility. This can have immediate applications during its construction, operation and use/ re-use phases.

2016-09

<M. Fischer, S. Savarese> <Spatiotemporal Semantic Understanding of Facilities> 2

Contents Glossary .......................................................................................................................................... 3

Engineering or Business Problem ................................................................................................... 4 Theoretical and Practical Points of Departure ................................................................................ 5

Research Methods and Work Plan .................................................................................................. 6 Expected Results: Findings, Contributions, and Impact on Practice .............................................. 9

Industry Involvement ...................................................................................................................... 9 Research Milestones and Risks ....................................................................................................... 9

Next Steps ..................................................................................................................................... 10 Budget ........................................................................................................................................... 11

References ..................................................................................................................................... 12


Glossary affordance : the qualities or properties of an object that define its possible uses or

make clear how it can or should be used e.g. We sit or stand on a chair because those affordances are fairly obvious.

lifecycle : a series of stages through which something (as an individual, culture, or manufactured product) passes during its lifetime e.g. A building has a construction, use and demolition phase during its lifecycle.

(3D) point cloud : a large collection of points in space acquired by 3D laser scanners or other technologies to create 3D representations of existing objects/ structures.

RGB, RGB-D data : 2D data consisting of images with RGB color channel, 2.5D data consisting of images with RGB color channel and Depth data.

semantics : the meaning or relationship of meanings of a sign or set of signs : in the context of a building, semantics correspond to the composing elements (e.g. walls, floors, doors, furniture, etc.) and their in-between relationships.

spatiotemporal : having both spatial and temporal qualities : of or relating to space-time

state (noun) : the particular condition of someone or something at a specific time

structurize : to give a structure to (something); to organize structurally.


Engineering or Business Problem Designers, project managers, superintendents, suppliers, owners and operators are all affected by the availability of accurate and up-to-date documentation of a building’s state in space and through time; what essentially constitutes a spatiotemporal 3D semantic model. Such a model includes information about a facility’s components and their in-between relationships over lifecycle phases. For example, during construction this information enables quality control, progress determination, incorporation of design changes, checking if a prefabricated system fits on site and accurate supply scheduling. During delivery to client, detailed as-built documentation can be handed over. During operation and maintenance, it can support activities especially when major maintenance needs to happen fast (e.g. plant turnarounds). During refurbishment, it can drive decision making for design purposes and reconfiguration of spaces. Hence spatiotemporal semantic information can impact the majority of a facility’s lifecycle phases and involved professionals (Fig. 1). Based on the above, such information can influence the following aspects of a facility: (a) Buildability, by facilitating construction processes (e.g. construction progress monitoring). (b) Operability, by guiding the operation and maintenance of a facility with information about visible building elements and those hidden during construction, and the timeline of modifications (less operating costs). (c) Sustainability, by making future refurbishment projects easier with complete and up-to-date digital models of the facility; by enabling accurate supply scheduling (no surplus of quantities, less transportation of materials); by containing easily incorporated information to energy efficiency models. In addition, it provides data for decision making related to controlling and inspecting productivity (e.g. crew productivity analysis), quality (e.g. deviations from the as-designed building plans) and safety (e.g. providing the location of hidden hazards), which alleviates risks and opens new pathways for owners, operators, designers and builders.

Fig. 1. Timeline of a building’s lifecycle and processes that require critical information.

According to the US National Building Information Model Standard Project Committee “Building Information Modeling (BIM) is a digital representation of physical and functional characteristics of a facility. A BIM is a shared knowledge resource of information about a facility forming a reliable basis for decisions during its life-cycle” [9]. Currently, the vast majority of BIMs reflect the design status and ignore construction and operation phases. Although some physical characteristics primarily related to the as-built status are obtained manually, BIMs cannot form a reliable basis for decision making throughout a facility’s lifecycle. One of the reasons of incomplete documentation is the time and cost of maintaining current and accurate information across lifecycle phases. A recent report from the National Institute of Standards and Technology (NIST) [13]


suggests that “an inordinate amount of time is spent locating and verifying specific facility and project information from previous activities”. The estimated cost of inadequate interoperability for a building’s lifecycle is around $15.8 billion, most of which is spent in the operation ($9 billion) and construction phases ($4 billion) [12]. Monitoring a building’s state has been facilitated the past years by off-the-shelf technologies that accurately capture the current status of a building (e.g. laser scanners). State-of-the-art practice involves iterative laser scanning and manual post-processing of the acquired data. The benefits of automating the surveying process with the use of scanners in terms of both cost and duration have been demonstrated the past few years in numerous cases in the industry. Such examples report not only acquiring an increased amount of additional information to traditional approaches, but also a cost reduction around 75% and a project duration reduction of approximately 60% [1,2]. Despite knowing the potential of laser scanning, it is still not widely employed. For example, construction progress continues to be recorded manually on tablets or hardcopies, which indicates that the output of laser scanners is not immediately useful to the AEC-FM industry since it requires substantial post-processing to acquire information that is easier to record with pen-and-paper, even if the former would be more accurate and richer in content. The reason for this is that 3D point clouds contain no high level information about the building elements present (type, location, quantity, shape, etc.) and are temporally fragmented (scanned data are uncorrelated over time). Since there is no automation in extracting this information, it is identified manually, which is a time-consuming and error-prone task [3], especially in large-scale buildings. For example, it took 200 hours to model the stainless pipes in a 14,000 sq. ft. chemical plant’s point cloud [38] and 180 hours to model steel components on a ⅓ mile-long pipe rack’s point cloud [39]. Although the modeling process can be accelerated with the use of tools such as Imaginit’s ‘Scan-to-BIM’ and EdgeWise’s ClearEdge 3D, there is still a significant amount of manual work involved. It is therefore evident that there is a gap between point cloud representations and the type of information required by workflows, which in combination with the extensive manual inference required, it impedes their direct use as an input. Hence, there is a need for automatic ways that will allow the generation of data of such structure and representational power that could be used as an input in a variety of AEC-FM related processes and proven to be substantially beneficial in order to become standard practice.

Theoretical and Practical Points of Departure Motivated by the limitations of current practice, research efforts regarding Automation in Construction have been working on the problem of automatically extracting semantic information from 3D point clouds. However, the majority of such work focuses on specific temporal points throughout the lifecycle of the building or specific building elements (e.g. MEP), which results to methods that are temporally fragmented and task-specific [14, 15, 16, 17, 10]. This approach of at best acquiring a discontinuous log of the building’s status is coming in contrast to the BIM outline [9] explained above. A detailed overview of the state of research for as-built modeling can be found in [40]. Relevant work with regard to understanding semantics in point clouds of indoor spaces1 can be found in the domain of Computer Vision as well. One of the key research topics is bridging the semantic gap between human-understandable rich high-level semantics and machine generated 1 Outdoor spaces, which are outside the scope of this proposal, have been vastly explored as well, however due to the nature of data, objects present and their configuration different types of methods are usually developed.


simple low-level features. A great amount of research has been conducted in the field of semantic understanding of 2D - RGB [18, 19, 20, 21] or 2.5D - RGB-D [22, 23, 24, 25, 26, 27] data of indoor spaces. However, these approaches remain small-scale and cannot address the new challenges that large-scale 3D scans of buildings pose, such as the availability of richer geometric information, the greater complexity and the introduction of new semantics. Also, the temporal aspect/dimension is either absent or of very small duration. Work on 3D point clouds of indoor spaces can be usually placed under one or more of the following categories: small-scale [28], of different goal (e.g. floorplan estimation [29, 30, 31, 32]), of different semantic output [33, 34], challenged in highly-cluttered scenes [29], with prior information requirement [30, 26, 35], etc. As a first step we recently proposed a method2 for semantically parsing large-scale point clouds of indoor as-built/ as-occupied spaces [37] (Fig. 2), with results that surpass the state-of-the-art in performance. This work addresses only part of a building’s timeline and does not include the construction phase. Since the algorithm is not trained on data from the latter, it would fail to understand the scene, mainly because of the different geometry and appearance of the elements, as well as the introduction of new elements.

Fig. 2. Semantic parsing of a large-scale point cloud. Left: raw data. Middle: results of parsing the point cloud into disjoint spaces (i.e. the floor plan). Right: results of parsing a detected room (black circle) into semantic elements.

From the above, it appears there is a lack of end-to-end methods that address the problem of semantically understanding a scene throughout long-term intervals, with varying elements, element appearance and geometry. Moreover, the potential impacts on the industry from obtaining such knowledge have not been quantified or fully explored beyond intuitions arising from evident applications.

Research Methods and Work Plan We propose to develop a method that builds a seamless bridge through time between the captured virtual environment and the real world via a single framework, by understanding the spatial state of a facility during its lifecycle, as depicted in 3D point cloud data. In a higher level, we want to be able to automatically and quantitatively answer questions regarding the building elements (which, how many, where, what size, etc.) at any time, and specifically from the point of construction that the main structural frame is erected3 onwards (Fig. 3).

2 The project’s website can be found at: buildingparser.stanford.edu 3 Previous temporal points (e.g. excavation, etc.) present substantial differences in contrast to a facility’s transformation after the structural frame is erected. For the proposal to remain feasible, we decided to limit our focus.


The main research question is: Which processes and parameters can drive the automated extraction of spatiotemporal physical characteristics (semantics) of buildings from 3D point clouds with the aim to positively influence several workflows in the AEC- FM domain, such as construction progress monitoring, as-built documentation, facilities management and refurbishment? The proposed approach is based on the structuration of raw point cloud data and their correlation over time. By focusing on both spatial and temporal aspects, a timeline of the facility becomes available that provides valuable insight regarding the building elements, such as data of pre-renovation status or of elements hidden during construction.

Fig. 3. Temporal area of research. The area marked in red delimits our focus in relation to the building’s lifecycle.

> Research method: The proposed research aims to structurize collected raw data and provide to the virtual world a close to human-level understanding of the visible real world. The main goal is to use this information directly or after post-processing in order to positively influence current workflows (Fig. 4).

Fig. 4. Schematic pipeline of proposed model

Input/ Raw data: The main input to the model is colored 3D point cloud data of one temporal point. We will also use knowledge produced in previous temporal points, if available. Other input data we will examine is introducing prior information available in as-designed plans. However, this data will not be indispensable for the process to take place, since we want to be able to achieve high accuracy even in cases where there is no access to such information. Structuration: To structure the data we will learn classifiers that can accurately and robustly parse the input to the seeked building elements (spatial semantics) and incorporate the output of previous temporal points (temporal semantics) or other data (e.g. as-designed plans) to guide the parsing. Previous experience has shown that parsing by detection in such context provides better results


and a more structured output. Common detection frameworks are based on sliding window approaches. The representation of detections can vary from low dimensional features to deep learning ones, but this decision will depend on the quantity and quality of data available. The final classifier will be able to make decisions about the object in the scene on a local level based on the generated features, on a contextual level by taking into account nearby elements, and on a temporal level by consolidating information available from previous temporal points. Output Data: The output is the parsed raw data in terms of building elements. For one iteration of the algorithm the output will consist of the elements present in the facility during that specific temporal point; for the complete process it constitutes a timeline of building components from the first iteration until the last. If we choose a detection-based approach, the main output is bounding boxes. To account for the lack of shape and geometry information of regular bounding boxes, we will use different degrees of granularity and voxelize the detections. A simple visualization of these voxelized detections looks very similar to a 3D model which allows a first understanding of the depicted scene’s semantics without the need for precise geometry modeling. Also, this allows a simplification of the raw data from millions of points to just hundreds of vertices and surfaces, and an easier comparison to the as-designed plans (geometry to geometry vs. points to geometry). Processing and Analysis & Workflows Influenced: The output can have several applications in important workflows related to construction, facilities management and refurbishment (for concrete examples see first section). An intermediate step of processing and/or analysis of the output might be required depending on the task (e.g. to compare as-designed with as-is states).

> Work Tasks Data Collection: Due to the lack of available datasets that can serve this research’s goals, we will perform data collection. We will collect raw data from large-scale buildings in different temporal points after the erection of the main structural frame onwards, capturing any element that can be subsequently hidden (e.g. pipes inside walls). This data collection will be performed with depth sensors or RGB-D cameras. Data Annotation: In order to train the algorithm, we need to provide ground truth information of how the real world looks like. As a result, we will annotate the raw input data by assigning semantic labels to the elements we will detect. The labels will correspond to commonly found elements in facilities during the discussed lifecycle phases, such as walls, floors, ceilings, beams, columns, doors, windows, MEP elements, wall framing, etc. and will account for their different appearance over time. Training and testing: Different learning approaches will be examined based on the algorithm's design goals and availability of data. The performance, limitations and degree to which they serve the framework will be carefully considered. For the algorithm to learn how to predict in a generalized way different data will be used during training and testing. Algorithm evaluation: To evaluate the performance of the algorithm we will compare the results to the obtained ground-truth data. We will also compare it with different variations of the algorithm (e.g. with the use of one or more types of raw data) and with baseline methods available in the literature to prove its significance. To quantify the comparisons, we will use appropriate and well-established metrics found in the literature, which choice depends on the learning approach and classifiers (e.g. mean average precision, intersection over union, ROC curves, etc.). Measuring Industry Impact: We will evaluate the effect of this method on above-mentioned workflows, in qualitative (questionnaires) and quantitative (by measuring and comparing values


such as time and cost required) ways. Since the scope of this step is to show the potential of the method, one case study per workflow is considered sufficient. During each case study we will compare the use of the proposed approach to current ones in the same scenario performed by expert professionals.

Expected Results: Findings, Contributions, and Impact on Practice We anticipate that an automated structuration of such data will (a) allow the direct input of raw data in workflows without the need for human intervention, (b) provide a more comprehensive understanding of the depicted environment and (c) spread the use of depth sensors throughout the lifecycle of a building since their applicability will be immediate and broad. Expected benefits to a few selected workflows are: • Construction progress monitoring: Reduce the time and cost of monitoring activities; facilitate

more frequent monitoring if needed, thus allowing managers to catch potential scheduling issues, improve day-to-day operations and report more current information to project stakeholders.

• Construction inspection: Automatically identify deviations between as-designed and as-built elements.

• Facility refurbishment/renovation: Provide a more complete understanding of existing conditions, including hidden elements, thus improving productivity and safety.

Although we will focus on indoor scenarios and building structures, there are outdoor projects that could benefit from this framework. For example, in solar farms, automated construction progress monitoring can have a high impact on current practice: managers have difficulty tracking progress due to the project scale and/or speed of construction. Apart from the evident applications, more can emerge, as for example acquiring space statistics about the volume, area, dimensions and number of spaces and elements in the facility, computing an illumination model by taking into account natural and artificial lighting or manipulating space and visualizing the changes (e.g. removing the separating wall between two adjacent rooms). The main contribution of this work is to further close the gap between the real and virtual world and facilitate a better understanding of our buildings throughout their lifecycle. By measuring its impact on a number of existing processes we will demonstrate the direct benefits to builders, operators, designers and clients. In addition, this research will serve as the point of departure for more in depth studies of built environments, such as those related to space optimization and decision making. Apart from the civil engineering domain, other communities will benefit as well, such as those of computer vision, robotics and augmented reality.

Industry Involvement The proposed project has been received with excitement from the construction industry, from companies such as Bouygues Construction, Mortneson Construction and DPR. Bouygues Construction will provide us with indoor data (thus performing the greatest part of data collection), expertise on BIM and construction, as well as case studies to test our proposed approach.

Research Milestones and Risks There are four milestones for this research: data collection, framework development, algorithm evaluation and case study implementation. The schedule for these activities is summarized below in Table 1. Data collection and annotation require a large amount of time to prepare, especially


taking into account that temporal data follow construction schedule. In preparation, we already started the data acquisition, which will continue throughout the project. We expect that within the first two months of the project we will have initial data ready to work with, and within the first five months enough data to run experiments. The next milestone involves formulating the spatiotemporal semantic understanding framework and running toy-examples to understand the problems and potential advantages of each approach (6 months). The third milestone is that of extensive evaluation of the algorithm on the collected data and comparison to state-of-the-art relative algorithms (2 months). The last milestone consists of the validation of the proposed method by comparing the output to current industry approaches (2 months).

Table 1. Timeline of Research Milestones (Months)

The identified risks are mainly related to the availability of data, so that we can reach a substantial amount of projects with objects of different type, appearance and geometry, something that will help with generalization. One more point of concern is case-study planning, primarily regarding finding facilities that are in the same point of their lifecycles as the examined process. For example, construction progress monitoring requires access during construction, facilities management when a building is operating, and refurbishment when a building will be renovated. To mitigate this risk, we have developed relationships with several industry partners who have pledged to provide us with the required data and access.

Next Steps One possible extension is considering the additional value of fusing point clouds with more data modalities (e.g. RGB and/or thermal images) as input to the algorithm, as well as the degree to which it affects performance or provides more information to the final model. Apart from enriching the proposed algorithm, we also have plans extending the framework. We mentioned above that a BIM contains the physical and functional characteristics of a building. This proposal is concerned only with the physical characteristics of the built environment. Future work will extend the understanding of our indoor spaces beyond the composing elements and their relationships, by adding human behavior. This consists of understanding the functional affordances (how/ how much/ in which way, etc.) involved in the interaction of occupants with spaces and the elements consisting a space, which can inform facilities management of efficiency, problems and deprecation in the building’s usage or drive function-specific space optimization. Currently such space utilization studies in practice are performed manually and over a short period of time. The study performed by the School of Engineering in Stanford is such an example. Automated methods would allow to collect data over longer time frames, get more accurate information and thus obtain a better understanding of how space is utilized.


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Summary for CIFE Seed Proposals for Academic Year 2016-17 ... · Type of innovation Breakthrough Abstract (up to 150 words) Understanding a building’s state in space and through