Optimizing burst-heavy datacenter workloads necessitates finegrained network control and visibility. We introduce CSIG, a protocol that delivers precise, multi-bit bottleneck congestion signals via a fixed-length Ethernet header. The architecture captures μsgranularity switch metrics, such as available bandwidth, and signals them to end-hosts using in-band, line-rate operations. We propose Fast Ramp-Up, a congestion control primitive that leverages these bottleneck signals to reduce median RPC latency by 20% and unclaimed bandwidth by 60% in production. Beyond transport-level performance, CSIG enables flow-aware observability by embedding μs-scale metrics into every packet, allowing individual application transfers to pinpoint their bottleneck location, such as the topology tier limiting their performance. CSIG thus transforms network telemetry from post-hoc correlation into a real time, context-aware capability. We demonstrate CSIG's broad deployability by validating it across five generations of commodity switch hardware (up to 102.4 Tbps), four NIC generations, and five transport stacks. Our design proves that a streamlined Layer 2 approach, focusing exclusively on the principal path bottleneck, provides transport-agnostic gains without requiring forklift hardware upgrades.

Outdoor AR applications on mobile devices need accurate estimates for the pose of the device. In this paper, we develop SplatPose, a novel pose estimation technique that uses a data-driven 3D modeling technique called Gaussian Splatting. SplatPose uses a trained Gaussian Splatting model to render an image at an estimated device location, then matches features with the camera image to estimate pose. % Because this matching can be fast, SplatPose can, in theory, estimate pose entirely on a mobile device, while existing approaches cannot. To this end, SplatPose trains Gaussian Splatting models to be robust to appearance changes, thereby improving accuracy. It also incorporates a novel fast renderer to improve rendering speed. Using an AR pose estimation benchmark dataset, we show that SplatPose outperforms the state-of-the-art in terms of accuracy, and is up to an order of magnitude faster on a mobile device.

On-vehicle 3D sensing technologies, such as LiDARs and stereo cameras, enable a novel capability, 3D traffic reconstruction. This produces a volumetric video consisting of a sequence of 3D frames capturing the time evolution of road traffic. 3D traffic reconstruction can help trained investigators reconstruct the scene of an accident. In this paper, we describe the design and implementation of RECAP, a system that continuously and opportunistically produces 3D traffic reconstructions from multiple vehicles. RECAP builds upon prior work on point cloud registration, but adapts it to settings with minimal point cloud overlap (both in the spatial and temporal sense) and develops techniques to minimize error and computation time in multi-way registration. On-road experiments and trace-driven simulations show that RECAP can, within minutes, generate highly accurate reconstructions that have 2× or more lower errors than competing approaches.

Tracking the position and orientation, or pose, of a viewing device enables AR applications to accurately embed virtual content in physical spaces. Mobile OSs track pose by matching device camera images against street-level imagery. Thus, pose tracking is often unavailable at off-street pedestrian locations. UbiPose enables pose tracking at such locations using aerial meshes, generated from satellite imagery, that are likely to be more widely available at these locations. However, matching a camera image against an aerial mesh can be error-prone, even with modern neural matchers. These neural components are also compute-intensive. UbiPose contains a novel pose tracking pipeline that runs entirely on a mobile device using fast-path optimizations designed to accept or reject pose estimates in many cases, without sacrificing accuracy. Experiments on real-world traces show that it achieves tracking accuracy comparable to AR pose tracking in iOS in places where that is available, and is able to track pose accurately in places where it is not.