Gartner® Recognizes Outsight as a Key Player in Spatial Computing

Frequently Asked Questions

• What is Spatial Computing and how is it different from traditional video analytics?

  Spatial Computing processes 3D sensor data to track the position, trajectory, and behavior of every person and vehicle across a physical site in real time. Traditional video analytics works on 2D image frames, which means it lacks native depth information and struggles in dense or occluded environments. The practical gap shows up in scale: a 2D camera system counts pixels, while a Spatial Computing system tracks discrete entities with centimeter-level spatial precision, assigns each a persistent anonymous ID, and predicts where bottlenecks will form before they occur. Gartner identifies Outsight as a key vendor in this space, and its SHIFT platform applies exactly this approach through infrastructure-based LiDAR deployments at sites such as Dallas Fort Worth Airport and BMW factories, processing roughly 4.7 billion 3D objects per year across five continents.

• Why does Gartner treat LiDAR as the primary sensor for smart-space 3D imaging?

  Gartner projects that 80% of 3D imaging sensors for smart spaces will use LiDAR or mmWave radar as the primary vision imaging sensor by 2030. The underlying reason is physics: LiDAR emits laser pulses and measures return times, producing accurate depth data in any lighting condition without recording faces or license plates. mmWave radar shares the privacy property but delivers far coarser spatial resolution. For use cases that require tracking individual people through crowded terminals or intersections with centimeter precision, LiDAR is the only sensor that currently meets both the accuracy and the privacy requirements simultaneously. Outsight's SHIFT platform is built around precisely this combination, processing LiDAR point clouds through a sub-50ms pipeline to generate anonymous 3D motion data across deployments such as Dallas Fort Worth Airport and SNCF train stations in France.

• What does it mean for Spatial AI to run on CPUs instead of GPUs at the edge?

  Running perception and tracking workloads on CPUs rather than GPUs changes the economics of large-scale deployment substantially. GPU-based inference requires specialized hardware that draws significantly more power, generates more heat, and costs more per node. A CPU-capable pipeline can run on standard edge servers or industrial PCs already present at most infrastructure sites, reducing per-sensor capital expenditure and eliminating the need for dedicated GPU clusters in each terminal or building. Outsight's SHIFT platform is built around this principle: its sub-50ms end-to-end pipeline processes 3D LiDAR data on CPU-based edge hardware, which is a key reason infrastructure operators can scale to dozens of sensors across a large facility, as seen in deployments like Dallas Fort Worth, without a proportional jump in hardware budget.

• Which other companies appear alongside Outsight in Gartner's Spatial Computing vendor landscape?

  The Gartner Emerging Tech Impact Radar: Computer Vision report places Outsight in the Spatial Computing category alongside Nvidia, Meta, Alphabet, and Matterport. That peer set spans semiconductor platforms, consumer hardware, hyperscale cloud, and static 3D mapping software. Outsight occupies a distinct position among them: it is the only company in that list focused specifically on real-time, anonymous tracking of people and vehicle flows through fixed infrastructure, rather than on rendering, device ecosystems, or photogrammetric site capture. That focus is embodied in the Motional Digital Twin, a live 3D replica of movement across a physical site, delivered through the SHIFT platform and deployed across airports, train stations, factories, and smart-city intersections on five continents.

• How does on-premise edge processing change data sovereignty for infrastructure operators?

  When the full perception and analytics pipeline runs on hardware located inside the operator's own facility, raw 3D point-cloud data never leaves the site network. Only structured outputs (entity counts, dwell times, queue depths, anomaly alerts) need to travel to dashboards or external systems. Outsight's SHIFT platform is built on this principle: LiDAR sensors process and discard raw spatial data at the edge, and the Motional Digital Twin exposes only anonymous, structured telemetry to downstream systems. This matters for operators in regulated sectors: airports handling passenger data under aviation-security frameworks, hospitals under healthcare privacy rules, and datacenters under cyber-physical security mandates can all satisfy data-residency requirements without architectural exemptions, because the sensitive raw data is processed and discarded locally.

• What distinguishes a Spatial Computing recognition from a Digital Twin recognition in analyst reports?

  Analyst firms treat Digital Twins as a data-architecture category: the question is whether an organization has a persistent, synchronized model of an asset or process. Spatial Computing is evaluated as a sensing-and-interaction category: the question is whether the technology can perceive, understand, and act on physical environments in real time. A system can build a digital twin without qualifying as Spatial Computing (a static BIM model is one example), and early Spatial Computing deployments have operated without a formal twin layer. Outsight's appearance in both Gartner categories reflects that its SHIFT platform bridges the two disciplines: it derives a live Motional Digital Twin directly from real-time spatial perception, using infrastructure-mounted LiDAR rather than device-worn sensors, producing a continuous 3D model of how people, vehicles, and robots move through a physical site.