Radars are an ideal complement to cameras: both are inexpensive, solid-state sensors, with cameras offering fine angular resolution, while radars provide metric depth and robustness under adverse weather. However, radar data is more difficult to interpret than camera images and varies significantly between sensors, necessitating increased reliance on simulation for prototyping sensors and processing pipelines. Recent work treating radar reconstruction as a novel view synthesis problem has shown great promise in reconstructing radar-relevant geometry and simulating lowlevel radar data. However, such methods are constrained by the low spatial resolution of the underlying radar. To address this, we propose a unified differentiable renderer, RadarSim, which leverages the high angular resolution of RGB cameras to generate Doppler radar range images from a camera-initialized neural field. Using a novel data set of calibrated radar camera recordings from a custom handheld rig, we demonstrate that RadarSim produces sharper geometry and Doppler range frames than radar-only reconstructions.

The rapid adoption of volumetric capture technologies has created a pressing need for efficient storage and streaming of dynamic 3D content. Unfortunately, current compression standards often treat dynamic sequences as independent frames or rely on computationally expensive non-rigid registration, making them unsuitable for real-time applications or large-scale environments. In this paper, we present a novel end-to-end compression framework for dynamic Truncated Signed Distance Field volumes derived from captured 3D content, leveraging a representation that is temporally stable, easily parallelizable, and already widely used in scene reconstruction and volumetric fusion pipelines. We then adapt classic 2D video coding paradigms such as spatial coding via Discrete Cosine Transform and temporal coding using a real-time motion compensation pipeline to provide robust, real-time, and training-free encoding and decoding for 3D content. Extensive evaluations on human performance captures demonstrate that our codec achieves ~35% bitrate savings at equal distortion while operating in real time at 30 FPS, while stronger temporal coherence in large-scale synthetic environments yields up to 12× bitrate reduction at equal distortion.

Wasm is gaining popularity outside the Web as a well-specified low-level binary format with ISA portability, low memory footprint and polyglot targetability, enabling efficient in-process sandboxing of untrusted code. Despite these advantages, Wasm adoption for new domains is often hindered by the lack of many standard system interfaces which precludes reusability of existing software and slows ecosystem growth.

mmWave radars are compact, inexpensive, and durable sensors that are robust to occlusions and work regardless of environmental conditions, such as weather and darkness. However, this comes at the cost of poor angular resolution, especially for inexpensive single-chip radars, which are typically used in automotive and indoor sensing applications. Although many have proposed learning-based methods to mitigate this weakness, no standardized foundational models or large datasets for the mmWave radar have emerged, and practitioners have largely trained task-specific models from scratch using relatively small datasets. In this paper, we collect (to our knowledge) the largest available raw radar dataset with 1M samples (29 hours) and train a foundational model for 4D single-chip radar, which can predict 3D occupancy and semantic segmentation with quality that is typically only possible with much higher resolution sensors. We demonstrate that our Generalizable Radar Transformer (GRT) generalizes across diverse settings, can be fine-tuned for different tasks, and shows logarithmic data scaling of 20\% per $10\times$ data. We also run extensive ablations on common design decisions, and find that using raw radar data significantly outperforms widely-used lossy representations, equivalent to a $10\times$ increase in training data. Finally, we roughly estimate that $\approx$100M samples (3000 hours) of data are required to fully exploit the potential of GRT.

We introduce Silverline, a novel framework for lightweight virtualization and orchestration of distributed real-time systems. Leveraging WebAssembly (Wasm) for robust sandboxing and multi-language (polyglot) capabilities, Silverline decouples applications from their platforms through distinct manifests, enabling a centralized orchestrator to optimize resource allocation and deploy Wasm modules seamlessly across the edge-cloud continuum. It features a split data and control plane with orchestration sidecars, allowing applications to use native communication protocols and respond autonomously to network changes. We evaluate our framework in two real application contexts: an industrial automation use-case and an automotive body electronics demonstrator. Through micro-benchmarks and end-to-end testing, we demonstrate Silverline's potential for managing real-time workloads in diverse heterogeneous ecosystems.

Heisenbugs , notorious for their ability to change behavior and elude reproducibility under observation, are among the toughest challenges in debugging programs. They often evade static detection tools, making them especially prevalent in cyber-physical edge systems characterized by complex dynamics and unpredictable interactions with physical environments. Although dynamic detection tools work much better, most still struggle to meet low enough jitter and overhead performance requirements, impeding their adoption. More importantly however, dynamic tools currently lack metrics to determine an observed bug’s “difficulty” or “heisen–ness” undermining their ability to make any claims regarding their effectiveness against heisenbugs. This paper proposes a methodology for detecting and identifying heisenbugs with low overheads at scale, actualized through the lens of dynamic data-race detection. In particular, we establish the critical impact of execution diversity across both instrumentation density and hardware platforms for detecting heisenbugs; the benefits of which outweigh any reduction in efficiency from limited instrumentation or weaker devices. We develop an experimental WebAssembly-backed dynamic data-race detection framework, Beanstalk, which exploits this diversity to show superior bug detection capability compared to any homogeneous instrumentation strategy on a fixed compute budget. Beanstalk’s approach also gains power with scale , making it suitable for low-overhead deployments across numerous compute nodes. Finally, based on a rigorous statistical treatment of bugs observed by Beanstalk, we propose a novel metric, the heisen factor , that similar detectors can utilize to categorize heisenbugs and measure effectiveness. We reflect on our analysis of Beanstalk to provide insight on effective debugging strategies for both in-house and in deployment settings.

Simulation is an invaluable tool for radio-frequency system designers that enables rapid prototyping of various algorithms for imaging, target detection, classification, and tracking. However, simulating realistic radar scans is a challenging task that requires an accurate model of the scene, radio frequency material properties, and a corresponding radar synthesis function. Rather than specifying these models explicitly, we propose DART - Doppler Aided Radar Tomography, a Neural Radiance Field-inspired method which uses radar-specific physics to create a reflectance and transmittance-based rendering pipeline for range-Doppler images. We then evaluate DART by constructing a custom data collection platform and collecting a novel radar dataset together with accurate position and instantaneous velocity measurements from lidar-based localization. In comparison to state-of-the-art baselines, DART synthesizes superior radar range-Doppler images from novel views across all datasets and additionally can be used to generate high quality tomographic images.11Our implementation, data collection platform, and collected datasets can be found via our project site: https://wiselabcmu.github.io/dart/.