← 研踪 PaperTrailarXiv:2608.09579 · v2
You Only Flow Once: Calibrated and Real-Time Radar Pose Estimation with Multi-Hypothesis Normalizing Flows
首次提交 2026-08-10 21:15
版本更新 2026-11-03 16:52
雷达观测 2026-11-04 23:39
摘要
Sparse and noisy millimeter-wave radar point cloud observations often correspond to multiple plausible human poses, making deterministic pose estimation fundamentally ill-posed. Yet existing radar methods remain deterministic, collapsing this ambiguity into a single estimate. Diffusion-based alternatives can model multi-hypothesis distributions but require costly sequential denoising for each sample and lack calibrated uncertainty. We propose Multi-Hypothesis Normalizing Flow Pose Generator (MH-NFPG), which models pose distributions from radar point clouds using a conditional normalizing flow. Specifically, we combine a spatiotemporal transformer backbone with a normalizing flow that transforms a Laplace base distribution into an expressive posterior, generated in parallel through a single forward pass. Leveraging this efficiency, we outperform diffusion-based alternatives in calibration across three radar benchmarks (MM-Fi, mmRadPose, mRI) at comparable pose accuracy, while achieving up to 22x faster inference and reducing calibration error by up to 84%. We find that calibration degrades substantially for diffusion models, whereas our flow-based approach maintains reliable coverage, also across environments. These results demonstrate normalizing flows as a practical alternative to diffusion models for real-time, uncertainty-aware radar pose estimation. Our code is available at https://github.com/jonasmueler/YouOnlyFlowOnce_WACV.
作者备注：Accepted at the Winter Conference on Applications of Computer Vision (WACV) 2027
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v2 · 2026-11-03 16:52 · 最新观测版本
You Only Flow Once: Calibrated and Real-Time Radar Pose Estimation with Multi-Hypothesis Normalizing Flows
Sparse and noisy millimeter-wave radar point cloud observations often correspond to multiple plausible human poses, making deterministic pose estimation fundamentally ill-posed. Yet existing radar methods remain deterministic, collapsing this ambiguity into a single estimate. Diffusion-based alternatives can model multi-hypothesis distributions but require costly sequential denoising for each sample and lack calibrated uncertainty. We propose Multi-Hypothesis Normalizing Flow Pose Generator (MH-NFPG), which models pose distributions from radar point clouds using a conditional normalizing flow. Specifically, we combine a spatiotemporal transformer backbone with a normalizing flow that transforms a Laplace base distribution into an expressive posterior, generated in parallel through a single forward pass. Leveraging this efficiency, we outperform diffusion-based alternatives in calibration across three radar benchmarks (MM-Fi, mmRadPose, mRI) at comparable pose accuracy, while achieving up to 22x faster inference and reducing calibration error by up to 84%. We find that calibration degrades substantially for diffusion models, whereas our flow-based approach maintains reliable coverage, also across environments. These results demonstrate normalizing flows as a practical alternative to diffusion models for real-time, uncertainty-aware radar pose estimation. Our code is available at https://github.com/jonasmueler/YouOnlyFlowOnce_WACV.
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