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Creators/Authors contains: "Kadambi, Achuta"

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  1. ; ; (, SPIE)
    Prussing, Keith F; Manser, Kimberly E; De_Melo, Celso; Rao, Raghuveer M; Howell, Christopher L (Ed.)
    Full Text Available 
  2. ; ; ; ; ; ; ; ; ; (, The 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; ; ; ; (, IEEE)
    Full Text Available 
  4. ; ; ; ; (, Nature Machine Intelligence)
    Full Text Available 
  5. ; ; ; ; (, International Conference on Computational Photography)
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  6. ; ; ; ; ; ; ; ; (, Synthetic Generation of Face Videos with Plethysmograph Physiology)
    Full Text Available 
  7. ; ; ; (, IEEE International Conference on Computational Photography)
    We propose a novel non-line-of-sight (NLOS) imaging framework with long-wave infrared (IR). At long-wave IR wavelengths, certain physical parameters are more favorable for high-fidelity reconstruction. In contrast to prior work in visible light NLOS, at long-wave IR wavelengths, the hidden heat source acts as a light source. This simplifies the problem to a single bounce problem. In addition, surface reflectance has a much stronger specular reflection in the long-wave IR spectrum than in the visible light spectrum. We reformulate a light transport model that leverages these favorable physical properties of long-wave IR. Specifically, we demonstrate 2D shape recovery and 3D localization of a hidden object. Furthermore, we demonstrate near real-time and robust NLOS pose estimation of a human figure, the first such demonstration, to our knowledge. 
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  8. ; ; ; ; ; ; ; (, IEEE Transactions on Pattern Analysis and Machine Intelligence)
    null (Ed.)
    We present a novel time-resolved light transport decomposition method using thermal imaging. Because the speed of heat propagation is much slower than the speed of light propagation, transient transport of far infrared light can be observed at a video frame rate. A key observation is that the thermal image looks similar to the visible light image in an appropriately controlled environment. This implies that conventional computer vision techniques can be straightforwardly applied to the thermal image. We show that the diffuse component in the thermal image can be separated and, therefore, the surface normals of objects can be estimated by the Lambertian photometric stereo. The effectiveness of our method is evaluated by conducting real-world experiments, and its applicability to black body, transparent, and translucent objects is shown. 
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    Full Text Available