The Human Race: Real-Time Rendering and Augmented Reality in the Movies and Beyond

Back in May at GDC 2017 Epic Games presented a revolutionary pipeline for rendering visual effects in real-time using their Unreal Engine. Developed in partnership with visual effects studio The Mill, the outcome of the project was a short promotional video for Chevrolet called The Human Race (above). While the film’s visual effects are stunning the underlying innovation isn’t immediately apparent. The following film by The Mill’s Rama Allen nicely summarises the process.

Behind the visual effects The Mill have an adjustable car rig called The Blackbird. Mounted on the car is a 360 degree camera rig which uses The Mill’s Cyclops system to stitch the video output from different cameras together and transmits it to Unreal Engine. Using positioning data from the The Blackbird and QR-like tracking markers on the outside of the vehicle as a spatial reference, the Unreal Engine then overlays computer generated imagery in real-time. Because all of this is being done in real-time a viewer can interactively reconfigure the virtual model of the car that has been superimposed on the The Blackbird rig while they are watching.

For the film industry this means that CGI and visual effects can be tested on location. For audiences it might mean that aspects of scenes within the final film become customisable. Perhaps the viewer can choose the protagonists car. Perhaps the implications are wider. If you can instantly revisualise a car or a character in the film why not an entire environment? With the emergence of more powerful augmented reality technologies, will there be a point at which this becomes a viable way to interact with and consume urban space?

The videos The Human Race and The Human Race – Behind The Scenes via Rama Allen and The MIll.

Habitat: Drama and Adventure in Early Online World’s

Today while trawling the web I stumbled on this promo for an early MMO from 1986 called Habitat. Produced by Lucasfilm Games in collaboration with online service provider Quantum Link the game provided for real-time interaction between Commodore 64 users via dial-up modem. The advert repeatedly assures potential players of the possibilities for fast paced ‘drama and adventure’, but its hard to get that sense from the in game footage. The appeal of the game likely had more to do with the novelty of synchronous interaction between players in a persistent and graphically represented online environment. To that point online MUDs had tended to be text based.

I love the way the players’ interactions are presented in the video. Combined with the jaunty music and voice over they seem jarringly innocent. At the same time the implications of their interactions weirdly presage the tensions and less comfortable aspects of online interaction today. While stealing another avatar’s head is totally fair game [IT TOTALLY IS!], the suggestion that an armed robbery in the game might be averted by a trip to the sauna…from the perspective of a critical reading there’s a lot going on there. I believe today’s augmented audiences are a little less naive.

Bang!

The game and its advert are wonderful artefacts from the perspective of media archeology, not only insofar as the game is a precursor to today’s MMOs, but also as it relates to the wider context of early developments in online communities, virtual environments and social media. The Museum of Art and Digital Entertainment (MADE) have obtained the original source code from Lucasfilm Games in an attempt to preserve and restore the game in working condition. The code is available on GitHub here.

Both the advert and the game provide wonderful artefacts for critical reading and media archeologies. The Museum of Art and Digital Entertainment (MADE) have obtained the original source code from Lucasfilm Games in an attempt to preserve and restore the game in working condition. The code is available on GitHub here.

Point Cloud Gaming: Scanner Sombre in VR

Scanner Sombre is an exploration game which places the player in the depths of a pitch black cave system with nothing to guide them except an experimental headset and LiDAR like sensor enabling them to see in the dark. I first saw Scanner Sombre back in April at the EGZ Rezzed computer game trade fair. I immediately fell in love with the beautiful visual style which renders the environment as a point cloud. The visual style links cleverly to the central game mechanic by which the points representing the contours of the cave walls only appear through the player’s use of the scanning device, providing an eerily partial view of the environment.

Following the initial release for desktop PC in April the game’s makers Introversion Software have just released a new VR version, now available on Steam for both Oculus Rift and HTC Vive. Having played the two I’d argue that players really have to try Scanner Sombre in VR to get the most out of the experience. Producer Mark Morris and designer Chris Delay touch on this in the following video which discusses the the process of transferring the desktop game to VR and the differences between the two. They also provide a very frank discussion of the factors contributing to the game’s poor sales relative to the runaway success of their earlier runaway success with Prison Architect.

One area that Mark and Chris discuss at length is narrative. The difficulty they discuss is providing the player sufficient motivation to play, and the pressure they felt to fit a narrative to the experience part way through development. At the same time they are uncertain that a more developed narrative would have added anything. I’d tend to agree. The unusual visual style and game mechanic have a niche feel which some players will love and some will hate. I love the VR version of the game but I could see others might feel it is more of an extended demo.

While Scanner Sombre has not met the designer’s expectations for sales I’ve found it a really enjoyable and atmospheric experience, particularly with the heightened sense of immersion provided by VR. If you’re interested in giving it a go you can currently pick it up for less that a fiver on Steam here.

Urban X-Rays: Wi-Fi for Spatial Scanning

Many of us in cities increasingly depend on Wi-Fi connectivity for communication as we go about our every day lives. However, beyond providing for our mobile and wireless communication needs, the intentional or directed use of Wi-Fi also provides new possibilities for urban sensing.

In this video professor Yasamin Mostofi from the University of California discusses research into the scanning or x-ray of built structures using a combination of drones and Wi-Fi transceivers. By transmitting a Wi-Fi signal from a drone on one side of a structure, and using a drone on the opposite side to receive and measure the strength of that signal it is possible to build up a 3D image of the structure and its contents. This methodology has great potential in areas like structural monitoring for the built environment, archaeological surveying, and even emergency response as outlined on the 3D Through-Wall Imaging project page.

Particularly with regard to emergency response one can easily imagine the value of being able to identify people trapped or hiding within a structure. Indeed Mostofi’s group are have also researched the potential these techniques provide for monitoring of humans in their Head Counting with WiFI project as demonstrated with the next video.

What is striking is that this technique enables individuals to be counted without themselves needing a Wi-Fi enabled device. Several potential uses are proposed which are particularly relevant to urban environments:

For instance, heating and cooling of a building can be better optimized based on learning the concentration of the people over the building. Emergency evacuation can also benefit from an estimation of the level of occupancy. Finally, stores can benefit from counting the number of shoppers for better business planning.

Given that WiFi networks are available in many buildings, we envision that they can provide a new way for occupancy estimation, in addition to cameras and other sensing mechanisms. In particular, its potential for counting behind walls can be a nice complement to existing vision-based methods.

I’m fascinated by the way experiments like this reveal the hidden potentials already latent within many of our cities. The roll out of citywide Wi-Fi infrastructure provides the material support for an otherwise invisible electromagnetic environment designers Dunne & Raby have called ‘Hertzian Space’. By finding new ways to sense the dynamics of this space, cities can tap in to these resources and exploit new potentialities, hopefully for the benefit of both the city and its inhabitants.

Thanks to Geo Awesomeness for posting the drone story here.

Open3D: Crowd-Sourced Distributed Curation of City Models

Open3D is a project by the Smart Geometry Processing Group in UCL’s Computer Science department. The project aims to provide tools for the crowd-sourcing of large-scale 3D urban models. It achieves this by giving users access to a basic 3D data set and providing an editor enabling them to amend the model and add further detail.

The model that users start with is created by vertically extruding 2D building footprints derived from OpenStreetMap or Ordnance Survey map data. Access to the resulting the 3D model is granted using a viewer based on the Cesium javascript library for rendering virtual globes in a web browser. The interface allows users to select particular buildings to work on. As changes are made to the model with the Open3D editor they are parameterised behind the scenes. This means that each changes become variables in an underlying set of repeatable rules that form templates representing common objects such as different types of window or door. These templates can then be shared between users and reapplied to other similar buildings within the model. This helps facilitate collaboration between multiple users and speeds up model creation.

Crowd-sourcing 3D urban models is not new. As we saw in an earlier post on 3D Imagery in Google Earth, Google’s acquisition of SketchUp in 2006 enabled enthusiasts to model and texture 3D buildings directly from satellite imagery. These models could then be uploaded to the 3D Warehouse where they were curated by Google who choose the best models for inclusion in their platform. Despite the enthusiasm of the user community there were limits to the speed of progress and the amount of coverage that could be achieved. In 2012 Google sold SketchUp to engineeting company Trimble after adopting a more automated process relying on a combination of photogrammetry and computer vision techniques. We recently saw similar techniques being used by ETH Zurich in our last post on their project VarCity.

In this context the Open3D approach which heavily relies on human intervention may seem outdated. However, while the kinds of textured surface models that are created using automated photogrammetry look very good from a distance, closer inspection reveals all sorts of issues. The challenges involved in creating 3D models through photogrammetry include: (i) gaining sufficient coverage of the object; (ii) the need to use images taken at different times in order to achieve sufficient coverage; (iii) having images of sufficient resolution to obtain the required level of detail; (iv) the indiscriminate nature of the captured images in the sense that they include everything within the camera’s field of view, regardless of whether it is intended for inclusion in the final model or not. Without manual editing or further processing this can result in noisy surfaces with hollow, blob-like structures for mobile or poorly defined structures and objects. The unofficial Google Earth Blog has done a great job of documenting such anomalies within the Google platform over the years. These include ghostly images and hollow objects, improbably deep riversdrowned citiesproblems with overhanging trees and buildings and blobby people.

The VarCity project sought to address these issues by developing new algorithms and combining techniques to improve the quality of the surface meshes they generated using aerial photogrammetry. For example, vehicle mounted cameras were used in combination with tourist photographs to provide higher resolution data at street level. In this way the ETH Zurich team were able to improve the level of detail and reduce noise in the building facades considerably. Despite this the results of the VarCity project still have limitations. For example, with regard to their use in first person virtual reality applications it could be argued that a more precisely modeled environment might better support a sense of presence and immersion for the user. While such a data set would be more artificial by virtue of the artifice involved in its production, it would also appear less jarringly course in appearance and feel more seamlessly realistic.

In their own ways both VarCity and Open3D seek to reduce the time and effort required in the production of 3D urban models. VarCity uses a combination of methods and increasingly sophisticated algorithms to help reduce noise in the automated reconstruction of urban environments. Open3D on the other hand starts with a relatively clean data set and provides tools to enhance productivity while leveraging the human intelligence of users and their familiarity with the environment they are modelling to maintain a high level of quality. Hence, while the current output for Open3D may appear quite rudimentary compared to VarCity this would improve through the effort of the systems potential users.

Unlike the VarCity project in which crowd-sourcing was effectively achieved by proxy through the secondary exploitation of tourist photos gathered via social media, Open3D seeks to engage a community of users through direct and voluntary citizen participation. In this regard Open3D has a considerable challenge. In order to work the project needs to find an enthusiastic user group and engage them by providing highly accessible and enjoyable tools and features that lower the bar to participation. To that end the Open3D team are collaborating with UCL’s Interaction Group (UCLIC) who will be focused on usability testing and adding new features. There is definitely an appetite for online creation of 3D which is evident in the success of new platforms like Sketchfab. Whether there is still sufficient enthusiasm for the bottom-up creation of 3D urban data sets without the influence of a brand like Google remains to be seen.

For more information on Open3D check out the Smart Geometry Processing Group page or have a look at the accompanying paper here.

VarCity: 3D and Semantic Urban Modelling from Images

In this video we see the results of a 5 year VarCity research project at the Computer Vision Lab, ETH Zurich. The aim of the project was to automatically generate 3D city models from photos such as those openly available online via social media.

The VarCity system uses computer algorithms to analyse and stitch together overlapping photographs. Point clouds are then created on the basis of overlapping points and then used to generate a geometric mesh or surface model. Other algorithms are used to identify and tag different types of urban objects like streets, buildings, roofs, windows and doors. These semantic labels can then be used to query the model to automatically determine meaningful information about buildings and streets as the video describes. In this way the VarCity project demonstrates one way in which comprehensive 3D city models could effectively be crowd sourced over time.

It is also interesting that VarCity is using computer vision to connect real-time video feeds or content from social media to actual locations. This is used to determine local vehicle and pedestrian traffic. As the video suggests, there may be limitations to this method for determining urban dynamics across the city as it is dependent of accessibility of a suitably large number of camera feeds. This also has implications for privacy and surveillance. The VarCity team address this by showing representative simulated views that replace actual scenes. As such the 3D modelling of urban regions can no longer be viewed as a neutral and purely technical enterprise.

The wider project covers four main areas of research:

  • Automatic city-scale 3D reconstruction
  • Automatic semantic understanding of the 3D city
  • Automatic analysis of dynamics within the city
  • Automatic multimedia production

A fuller breakdown of the VarCity project can be viewed in the video below.

The work on automatic 3D reconstruction is particularly interesting. A major difficulty with the creation of 3D city models has been the amount of manual effort they require to create and update through traditional 3D modelling workflows. One solution has been to procedurally generate such models using software such as ESRI’s CityEngine. With CityEngine preset rules are used randomly determine the values for parameters like the height of buildings, the pitch of the roofs, the types of walls and doors. This is a great technique for generating fictional cities for movies and video games. However, this has never been fully successful for the modelling of actually existing urban environments. This is because the outputs of procedurally generated models are only as good as the inputs, both the complexity of the rules used for generating the geometry, but also the representational accuracy of things like the models for street furniture and textures for buildings if they are to be applied.

Procedural generation also involves an element of randomness requiring the application of constraints such as the age of buildings in specific areas which determines which types of street furniture and textures should be applied. Newer districts may be more likely to feature concrete and glass whereas much older districts will likely consist of buildings made of brick. The more homogeneous an area is in terms of age and design the more easy it is to procedurally generate, especially if it is laid out in a grid. Even so there is always the need for manual adjustment which takes considerable effort and may involve ground truthing. Using such methods for particularly heterogeneous cities like London are problematic, especially if regular updates are required to capture changes as they occur.

For my own part I’m currently looking at the processing of point cloud information so it will be fascinating to read the VarCity team’s research papers, available here.

The Art & Science of 3D Cities at the Transport Systems Catapult

Back in March I attended a day long workshop the at the Transport Systems Catapult (TCS) in Milton Keynes on the subject of ‘The Barriers to Building 3D Synthetic Environments’. The aim of the workshop was to bring together key SMEs and Academics to collaboratively identify challenges and discuss solutions for the creation of virtual environments that would be suitable for simulating and testing transport scenarios.

Alongside presentations from the Transport Systems, Future Cities and Satellite Applications catapults a number of SMEs also presented on topics as diverse as LiDAR data capture, GNSS positioning, 3D GIS and the use of GIS data in game engines. For my purposes the following talk on ‘The Art & Science of 3D Cities’ by Elliot Hartley of Garsdale Design was particularly interesting and raised a number of great points:

One of the key challenges for the generation and use of 3D data discussed by Elliot derives from the heightened expectation generated by the depiction of 3D urban environments in films, video games and Google Earth. The truth is the creation of these kinds of environments require considerable investment in terms of time and investment. Elliot’s talk poses key questions for stakeholders when embarking on a 3D project:

  • Why do you want a 3D model?
  • Do you actually need a 3D model?
  • What kind of 3D model do you want?
  • What 3D model do you actually need?
    • Small areas with lots of detail?
    • Large areas with little detail?
  • How much time and/or money do you have?
  • Will you want to publish the model?
  • What hardware and software do you have?
  • What’s the consequence of getting the model wrong?

While the primary focuses for the day were the practical and technical challenges of creating 3D environments, the further implication of Elliot’s discussion is that the use of 3D data and the creation of virtual environments can no longer be considered a purely technical activity with neutral products and outputs. For me the last question in particular foregrounded the stakes involved in moving beyond visualisation toward the growing use of 3D data in various forms of analysis. Thanks to Elliot for the stimulating talk.

After the presentations we had a tour of the TCS facilities and then broke up into work groups to discuss a number of themes. A report and summary is expected to be published by the TCS soon.