aiqbench analysis
SPIRIT represents a significant advancement in AI-driven sensing by applying physics-informed inverse rendering to solve the speed-resolution trade-off in synthetic aperture radar (SAR) imaging. By explicitly modeling wave propagation and visibility constraints, this framework reconstructs accurate 3D geometry from sparse data while reducing collection requirements by over 90%. This work bridges advanced machine learning optimization with physical signal processing to enhance sensing capabilities under adverse conditions.
SPIRIT: Sparse Physics-Informed Inverse Rendering for SAR Imaging
arXiv:2610.23914v1 Announce Type: cross Abstract: Radio-frequency (RF) and wave-based imaging systems play a critical role in sensing under adverse conditions, but are fundamentally constrained by a trade-off between collection speed and spatial resolution. Reducing measurements to enable rapid collection of fragment geometric information across sensing dimensions, leading to highly non-injective inverse problems that destabilize conventional image-domain reconstruction and super-resolution methods. Synthetic aperture radar (SAR) exemplifies this challenge in sparse-aperture sampling. We introduce SPIRIT, a physics-informed inverse rendering framework for sparse-aperture SAR. Rather than enhancing formed images, SPIRIT reconstructs an explicit three-dimensional surface. By adopting an optimization strategy and explicitly modeling visibility, phase coherence, and wave propagation, SPIRIT aggregates distributed geometric features across sparse aperture views into a coherent reconstruction. Extensive experiments on synthetic and real-world data demonstrate that SPIRIT recovers accurate geometry and signal under extreme sparsity, reducing data collection by over 90% while achieving low geometric and signal error and consistently outperforming baselines.
Abstract:Radio-frequency (RF) and wave-based imaging systems play a critical role in sensing under adverse conditions, but are fundamentally constrained by a trade-off between collection speed and spatial resolution. Reducing measurements to enable rapid collection of fragment geometric information across sensing dimensions, leading to highly non-injective inverse problems that destabilize conventional image-domain reconstruction and super-resolution methods. Synthetic aperture radar (SAR) exemplifies this challenge in sparse-aperture sampling. We introduce SPIRIT, a physics-informed inverse rendering framework for sparse-aperture SAR. Rather than enhancing formed images, SPIRIT reconstructs an explicit three-dimensional surface. By adopting an optimization strategy and explicitly modeling visibility, phase coherence, and wave propagation, SPIRIT aggregates distributed geometric features across sparse aperture views into a coherent reconstruction. Extensive experiments on synthetic and real-world data demonstrate that SPIRIT recovers accurate geometry and signal under extreme sparsity, reducing data collection by over 90% while achieving low geometric and signal error and consistently outperforming baselines.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2610.23914 [cs.CV]
(or arXiv:2610.23914v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.23914 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 247 words