The paper introduces 3D Point Splatting (3DPS), a differentiable point renderer for millimeter-wave radar that directly implements the radar equation with an explicit material model and complex-valued outputs. Unlike prior methods, 3DPS simultaneously provides physical fidelity, complex-valued rendering, and multi-viewpoint tractability, enabling a single optimized scene to generate ADC, complex range profile, and range-azimuth images via FFT pipelines. On six outdoor ColoRadar scenes, 3DPS achieves a mean Pearson correlation of 0.587 on held-out range-azimuth images, outperforming optical-NVS baselines by 1.7x to 5.2x, and trains in about three minutes per scene on an RTX 4090.
The paper introduces 3D Point Splatting (3DPS), a differentiable point renderer for millimeter‑wave radar that directly implements the radar equation with an explicit material model and complex‑valued outputs. Unlike previous methods, 3DPS preserves phase information and supports multiple output formats (ADC, CRP, RA) without retraining. On six outdoor ColoRadar scenes, it achieves a mean Pearson correlation of 0.587 on held‑out RA images, outperforming optical‑NVS baselines by 1.7× to 5.2× and trains in roughly three minutes per scene on a single RTX 4090.
By Adnan Armouti, Yixuan Gao, Rajalakshmi Nandakumar
This study explores whether spatial structure can be learned directly from pre-beamforming per-antenna range-Doppler (RD) radar measurements, bypassing traditional beamforming steps. Using a 6‑TX × 8‑RX automotive radar with a chirp‑sequence FMCW transmit scheme, the authors train a dual‑chirp shared‑weight encoder on raw RD tensors and evaluate spatial recoverability via bird’s‑eye‑view occupancy maps. Experiments across different transmit configurations (A‑only, B‑only, A+B) and receive apertures demonstrate that meaningful spatial structure is indeed recoverable through learned spatial mixing, without hand‑crafted signal‑processing stages.
By George Sebastian, Philipp Berthold, Bianca Forkel, Leon Pohl, Mirko Maehlisch
arXiv:2611.08342v1 Announce Type: cross
Abstract: Millimeter-wave radar resolves the micrometer displacements of a moving body as micro-Doppler, enabling gait, gesture, activity, pose and vital-sign...
By Adnan Armouti, Guanyue Qian, Rajalakshmi Nandakumar
arXiv:2606. 28396v1 Announce Type: cross Abstract: Millimeter-wave (mmWave) radar perception is limited by data scarcity: models trained on existing radar datasets fail to generalize to new objects, environments, and sensing trajectories.
By Emily Bejerano, Federico Tondolo, Devang Gupta, Aaron Mano Cherian, Taeyoo Kim, Ayaan Qayyum, Xiaofan Yu, Xiaofan Jiang
DyRAD introduces a novel radar novel‑view synthesis framework that models dynamic driving scenes by separating static background reflectors from motion‑tracked dynamic point reflectors, enabling the rendering of full range‑azimuth‑Doppler (RAD) tensors. The method derives reflector velocities from object tracks, projects them onto the line of sight, and uses a fixed analytic point‑spread function to avoid embedding sensor‑induced spread into the scene representation. This design allows accurate scene reconstruction and zero‑shot transfer to different radar configurations, achieving a 90.7% recovery of radar detections on the RADIal dataset compared to 26.9% for the best baseline.
By Merav Keidar, Tomer Borreda, Rajalakshmi Nandakumar, Or Litany
arXiv:2609.34768v2 Announce Type: replace
Abstract: Millimeter-wave (mmWave) radar enables privacy-preserving human perception, but the extreme sparsity of point clouds from commercial single-chip se...
By Shuxing Zhang, Yongquan Ni, Zhenyu Ding, Yawen Lin
MITO is the first millimeter‑wave dataset featuring diverse everyday objects, collected with a UR5 robotic arm equipped with two mmWave radars and an RGB‑D camera. It includes over 24 million mmWave frames, 550 high‑resolution mmWave images in both line‑of‑sight and non‑line‑of‑sight conditions, along with RGB‑D images, segmentation masks, and raw signals for 76 objects. An open‑source simulator is provided to generate synthetic mmWave images from any 3D triangle mesh, and benchmarks for NLOS segmentation and classification are established to showcase the dataset’s utility.
By Tara Boroushaki, Laura Dodds, Cusuh Ham, Fadel Adib
arXiv:2605.11840v2 Announce Type: replace
Abstract: Radar camera depth estimation turns a very sparse but metric radar signal into a dense depth map. Existing methods combine radar and image features...
By Zhangcheng Hou, Tomoaki Ohtsuki
arXiv:2610.34085v1 Announce Type: cross
Abstract: Safe indoor operation requires autonomous tracking of objects that are not directly visible to the robot. Optical non-line-of-sight (NLoS) methods de...
By Yerin Cheon, Kush Garg, Akshat Dave
RLG-TPV introduces a multimodal Tri-Perspective View framework that fuses camera, radar, and training‑time LiDAR data for 3D object detection. It uses radar and LiDAR to guide a ray‑deformable attention lift, refining depth distributions and providing geometric supervision for side and front planes, while radar cross‑section awareness spreads evidence spatially. On nuScenes, the method attains 0.4981 mAP and 0.5959 NDS, improving orientation and velocity accuracy by about 32 % and 31 % over the CRN baseline.
By Ahmet Mete Dokgoz, A. Enes Doruk, Hasan F. Ates
arXiv:2609.24151v1 Announce Type: new
Abstract: Four-dimensional (4D) Radar has emerged as a key sensor for environmental perception, providing range, azimuth, elevation, and Doppler measurements whi...
By Seung-Hyun Song, Dong-Hee Paek, Seung-Hyun Kong