Robust and non-destructive material classification is a challenging but crucial first-step in numerous vision applications. We propose a physics-guided material classification framework that relies on thermal properties of the object. Our key observation is that the rate of heating and cooling of an object depends on the unique intrinsic properties of the material, namely the emissivity and diffusivity. We leverage this observation by gently heating the objects in the scene with a low-power laser for a fixed duration and then turning it off, while a thermal camera captures measurements during the heating and cooling process. We then take this spatial and temporal “thermal spread function” (TSF) to solve an inverse heat equation using the finite-differences approach, resulting in a spatially varying estimate of diffusivity and emissivity. These tuples are then used to train a classifier that produces a fine-grained material label at each spatial pixel. Our approach is extremely simple requiring only a small light source (low power laser) and a thermal camera, and produces robust classification results with 86% accuracy over 16 classes11Code: https://github.com/aniketdashpute/TSF.

The utilization of computational photography becomes increasingly essential in the medical field. Today, imaging techniques for dermatology range from two-dimensional (2D) color imagery with a mobile device to professional clinical imaging systems measuring additional detailed three-dimensional (3D) data. The latter are commonly expensive and not accessible to a broad audience. In this work, we propose a novel system and software framework that relies only on low-cost (and even mobile) commodity devices present in every household to measure detailed 3D information of the human skin with a 3D-gradient-illumination-based method. We believe that our system has great potential for early-stage diagnosis and monitoring of skin diseases, especially in vastly populated or underdeveloped areas.

The poor lateral and depth resolution of state-of-the-art 3D sensors based on the time-of-flight (ToF) principle has limited widespread adoption to a few niche applications. In this work, we introduce a novel sensor concept that provides ToF-based 3D measurements of real world objects and surfaces with depth precision up to 35 μm and point cloud densities commensurate with the native sensor resolution of standard CMOS/CCD detectors (up to several megapixels). Such capabilities are realized by combining the best attributes of continuous wave ToF sensing, multi-wavelength interferometry, and heterodyne interferometry into a single approach. We describe multiple embodiments of the approach, each featuring a different sensing modality and associated tradeoffs.

Phase-retrieval based wavefront sensors have been shown to reconstruct the complex field from an object with a high spatial resolution. Although the reconstructed complex field encodes the depth information of the object, it is impractical to be used as a depth sensor for macroscopic objects, since the unambiguous depth imaging range is limited by the optical wavelength. To improve the depth range of imaging and handle depth discontinuities, we propose a novel three-dimensional sensor by leveraging wavelength diversity and wavefront sensing. Complex fields at two optical wavelengths are recorded, and a synthetic wavelength can be generated by correlating those wavefronts. The proposed system achieves high lateral and depth resolutions. Our experimental prototype shows an unambiguous range of more than 1,000 x larger compared with the optical wavelengths, while the depth precision is up to 9µm for smooth objects and up to 69µm for rough objects. We experimentally demonstrate 3D reconstructions for transparent, translucent, and opaque objects with smooth and rough surfaces.

Three dimensional imaging techniques have been widely used in both industry and academia. Time-of-flight (ToF) sensors offer a promising method of 3D imaging due to compact size and low complexity. However, state-of-the-art ToF sensors only have depth resolutions of centimeters due to limitations in the modulation frequencies that can be used. In this paper, we propose a technique to generate modulation frequencies as high as 1 THz using optical superheterodyne interferometry. Our proposed system provides great flexibility in imaging range and resolution. We experimentally demonstrate an increase in depth resolution by an order of magnitude relative to currently available commercial ToF cameras.