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Petros Boufounos

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3 papers

1 author row

AAAI Conference 2026 Conference Paper

Indoor Multi-View Radar Object Detection via 3D Bounding Box Diffusion

Ryoma Yataka
Pu (Perry) Wang
Petros Boufounos
Ryuhei Takahashi

Multi-view indoor radar perception has drawn attention due to its cost-effectiveness and low privacy risks. Existing methods often rely on implicit cross-view radar feature association, such as proposal pairing in RFMask or query-to-feature cross-attention in RETR, which can lead to ambiguous feature matches and degraded detection in complex indoor scenes. To address these limitations, we propose REXO (multi-view Radar object dEtection with 3D bounding boX diffusiOn), which lifts the 2D bounding box (BBox) diffusion process of DiffusionDet into the 3D radar space. REXO utilizes these noisy 3D BBoxes to guide an explicit cross-view radar feature association, enhancing the cross-view radar-conditioned denoising process. By accounting for prior knowledge that the person is in contact with the ground, REXO reduces the number of diffusion parameters by determining them from this prior. Evaluated on two open indoor radar datasets, our approach surpasses state-of-the-art methods by a margin of +4.22 AP on the HIBER dataset and +11.02 AP on the MMVR dataset. Our implementation is available at https://github.com/merlresearch/radar-bbox-diffusion.

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NeurIPS Conference 2025 Conference Paper

RAPTR: Radar-based 3D Pose Estimation using Transformer

Sorachi Kato
Ryoma Yataka
Pu Wang
Pedro Miraldo
Takuya Fujihashi
Petros Boufounos

Radar-based indoor 3D human pose estimation typically relied on fine-grained 3D keypoint labels, which are costly to obtain especially in complex indoor settings involving clutter, occlusions, or multiple people. In this paper, we propose \textbf{RAPTR} (RAdar Pose esTimation using tRansformer) under weak supervision, using only 3D BBox and 2D keypoint labels which are considerably easier and more scalable to collect. Our RAPTR is characterized by a two-stage pose decoder architecture with a pseudo-3D deformable attention to enhance (pose/joint) queries with multi-view radar features: a pose decoder estimates initial 3D poses with a 3D template loss designed to utilize the 3D BBox labels and mitigate depth ambiguities; and a joint decoder refines the initial poses with 2D keypoint labels and a 3D gravity loss. Evaluated on two indoor radar datasets, RAPTR outperforms existing methods, reducing joint position error by $34. 3$\% on HIBER and $76. 9$\% on MMVR. Our implementation is available at \url{https: //github. com/merlresearch/radar-pose-transformer}.

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NeurIPS Conference 2024 Conference Paper

RETR: Multi-View Radar Detection Transformer for Indoor Perception

Ryoma Yataka
Adriano Cardace
Pu Wang
Petros Boufounos
Ryuhei Takahashi

Indoor radar perception has seen rising interest due to affordable costs driven by emerging automotive imaging radar developments and the benefits of reduced privacy concerns and reliability under hazardous conditions (e. g. , fire and smoke). However, existing radar perception pipelines fail to account for distinctive characteristics of the multi-view radar setting. In this paper, we propose Radar dEtection TRansformer (RETR), an extension of the popular DETR architecture, tailored for multi-view radar perception. RETR inherits the advantages of DETR, eliminating the need for hand-crafted components for object detection and segmentation in the image plane. More importantly, RETR incorporates carefully designed modifications such as 1) depth-prioritized feature similarity via a tunable positional encoding (TPE); 2) a tri-plane loss from both radar and camera coordinates; and 3) a learnable radar-to-camera transformation via reparameterization, to account for the unique multi-view radar setting. Evaluated on two indoor radar perception datasets, our approach outperforms existing state-of-the-art methods by a margin of 15. 38+ AP for object detection and 11. 91+ IoU for instance segmentation, respectively. Our implementation is available at https: //github. com/merlresearch/radar-detection-transformer.

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