This paper presents SurfRadar, a fully automatic mmWave system for in-the-wild surface characterization. SurfRadar operates on a mobile robot and estimates intrinsic surface properties including dielectric constant and roughness. Central to our approach is leveraging high-resolution imaging and analyzing coherent surface reflection images rather than raw radar signals. We develop a physics-based model that connects material parameters to these images. Through forward synthesis and backward optimization, SurfRadar recovers the intrinsic parameters that best explain the observed reflection profile. Our results demonstrate accurate characterization across 11 surface types, the ability to produce scene-level semantic maps, and applicability to other datasets.

Digital twins today are almost entirely visual, overlooking acoustics—a core component of spatial realism and interaction. We introduce AV-Twin, the first practical system that constructs editable audio-visual digital twins using only commodity smartphones. AV-Twin combines mobile RIR capture and a visual-assisted acoustic field model to efficiently reconstruct room acoustics. It further recovers per-surface material properties through differentiable acoustic rendering, enabling users to modify materials, geometry, and layout while automatically updating both audio and visuals. Together, these capabilities establish a practical path toward fully modifiable audio-visual digital twins for real-world environments. We provide a demo video for our system at https://youtu.be/k31nKDRhJJw.

This paper presents CartoRadar, a novel RF-based SLAM system that delivers high-fidelity 3D mapping with centimeter-level accuracy. CartoRadar builds on top of the advancements in learning-based RF imaging. However, learning-based systems often exhibit variation in prediction accuracy during inference. To address this challenge and enable robust RF sensing, CartoRadar introduces a novel, training-free uncertainty quantification method tailored to RF signals. Additionally, CartoRadar features an efficient SLAM algorithm that incorporates this uncertainty into the mapping process. We deploy CartoRadar on a mobile robot and conduct extensive evaluations across 14 floors in 5 buildings. Results show that CartoRadar achieves a trajectory error of 14.1 cm, outperforming camera-based baselines by 72.1%. For mapping, CartoRadar achieves an accuracy of 7.4 cm and a completion of 8.1 cm, improving over vision methods by 46.2% and 67.6%, respectively. Code, datasets, and demo videos are available on our website.

Seeing hidden structures and objects around corners is critical for robots operating in complex, cluttered environments. Existing methods, however, are limited to detecting and tracking hidden objects rather than reconstructing the occluded full scene. We present HoloRadar, a practical system that reconstructs both line-of-sight (LOS) and non-line-of-sight (NLOS) 3D scenes using a single mmWave radar. HoloRadar uses a two-stage pipeline: the first stage generates high-resolution multi-return range images that capture both LOS and NLOS reflections, and the second stage reconstructs the physical scene by mapping mirrored observations to their true locations using a physics-guided architecture that models ray interactions. We deploy HoloRadar on a mobile robot and evaluate it across diverse real-world environments. Our evaluation results demonstrate accurate and robust reconstruction in both LOS and NLOS regions. Code, dataset and demo videos are available on the project website.

This paper introduces PanoRadar, a novel RF imaging system that brings RF resolution close to that of LiDAR, while providing resilience against conditions challenging for optical signals. Our LiDAR-comparable 3D imaging results enable, for the first time, a variety of visual recognition tasks at radio frequency, including surface normal estimation, semantic segmentation, and object detection. PanoRadar utilizes a rotating single-chip mmWave radar, along with a combination of novel signal processing and machine learning algorithms, to create high-resolution 3D images of the surroundings. Our system accurately estimates robot motion, allowing for coherent imaging through a dense grid of synthetic antennas. It also exploits the high azimuth resolution to enhance elevation resolution using learning-based methods. Furthermore, PanoRadar tackles 3D learning via 2D convolutions and addresses challenges due to the unique characteristics of RF signals. Our results demonstrate PanoRadar's robust performance across 12 buildings. Code, datasets, and demo videos are available on our website.

This demo presents PanoRadar, a novel RF imaging system that brings RF resolution close to that of LiDAR, while providing resilience against conditions challenging for optical signals. Our LiDAR-comparable 3D imaging results enable, for the first time, a variety of visual recognition tasks at radio frequency, including surface normal estimation, semantic segmentation, and object detection. PanoRadar utilizes a rotating single-chip mmWave radar, along with a combination of novel signal processing and machine learning algorithms, to create high-resolution 3D images of the surroundings. Our system accurately estimates robot motion, allowing for coherent imaging through a dense grid of synthetic antennas. It also exploits the high azimuth resolution to enhance elevation resolution using learning-based methods. Furthermore, PanoRadar tackles 3D learning via 2D convolutions and addresses challenges due to the unique characteristics of RF signals. This demonstration illustrates the ability of high-resolution RF imaging of PanoRadar. We present the signal processing results to show the high range and azimuth resolution of the system, and the machine learning results for elevation resolution enhancement. Code, datasets, and demo videos are available on our website.

In the era of advancing autonomous driving and increasing reliance on geospatial information, high-precision mapping not only demands accuracy but also flexible construction. Current approaches mainly rely on expensive mapping devices, which are time consuming for city-scale map construction and vulnerable to erroneous data associations without accurate GPS assistance. In this article, we present AutoMerge, a novel framework for merging large-scale maps that surpasses these limitations, which: 1) provides robust place recognition performance despite differences in both translation and viewpoint; 2) is capable of identifying and discarding incorrect loop closures caused by perceptual aliasing; and 3) effectively associates and optimizes large-scale and numerous map segments in the real-world scenario. AutoMerge utilizes multiperspective fusion and adaptive loop closure detection for accurate data associations, and it uses incremental merging to assemble large maps from individual trajectory segments given in random order and with no initial estimations. Furthermore, AutoMerge performs pose graph optimization after assembling the segments to smooth the merged map globally. We demonstrate AutoMerge on both city-scale merging (120 km) and campus-scale repeated merging (4.5 km × 8). The experiments show that AutoMerge: 1) surpasses the second- and third-best methods by 0.9% and 6.5% recall in segment retrieval; 2) achieves comparable 3-D mapping accuracy for 120-km large-scale map assembly; and 3) and is robust to temporally spaced revisits. To our knowledge, AutoMerge is the first mapping approach to merge hundreds of kilometers of individual segments without using GPS.