Multi-camera interference (MCI) is an important challenge faced by continuous-wave time-of-flight (C-ToF) cameras. In the presence of other cameras, a C-ToF camera may receive light from other cameras' sources, resulting in potentially large depth errors. We propose stochastic exposure coding (SEC), a novel approach to mitigate MCI. In SEC, the camera integration time is divided into multiple time slots. Each camera is turned on during a slot with an optimal probability to avoid interference while maintaining high signal-to-noise ratio (SNR). The proposed approach has the following benefits. First, SEC can filter out both the AC and DC components of interfering signals effectively, which simultaneously achieves high SNR and mitigates depth errors. Second, time-slotting in SEC enables 3D imaging without saturation in the high photon flux regime. Third, the energy savings due to camera turning on during only a fraction of integration time can be utilized to amplify the source peak power, which increases the robustness of SEC to ambient light. Lastly, SEC can be implemented without modifying the C-ToF camera's coding functions, and thus, can be used with a wide range of cameras with minimal changes. We demonstrate the performance benefits of SEC with thorough theoretical analysis, simulations and real experiments, across a wide range of imaging scenarios.

For several vision and robotics applications, 3D geometry of man-made environments such as indoor scenes can be represented with a small number of dominant planes. However, conventional 3D vision techniques typically first acquire dense 3D point clouds before estimating the compact piece-wise planar representations (e.g., by plane-fitting). This approach is costly, both in terms of acquisition and computational requirements, and potentially unreliable due to noisy point clouds. We propose Blocks-World Cameras, a class of imaging systems which directly recover dominant planes of piece-wise planar scenes (Blocks-World), without requiring point clouds. The Blocks-World Cameras are based on a structured-light system projecting a single pattern with a sparse set of cross-shaped features. We develop a novel geometric algorithm for recovering scene planes without explicit correspondence matching, thereby avoiding computationally intensive search or optimization routines. The proposed approach has low device and computational complexity, and requires capturing only one or two images. We demonstrate highly efficient and precise planar-scene sensing with simulations and real experiments, across various imaging conditions, including defocus blur, large lighting variations, ambient illumination, and scene clutter.

As continuous-wave time-of-flight (C-ToF) cameras become popular in 3D imaging applications, they need to contend with the problem of multi-camera interference (MCI). In a multi-camera environment, a ToF camera may receive light from the sources of other cameras, resulting in large depth errors. In this paper, we propose stochastic exposure coding (SEC), a novel approach for mitigating. SEC involves dividing a camera's integration time into multiple slots, and switching the camera off and on stochastically during each slot. This approach has two benefits. First, by appropriately choosing the on probability for each slot, the camera can effectively filter out both the AC and DC components of interfering signals, thereby mitigating depth errors while also maintaining high signal-to-noise ratio. This enables high accuracy depth recovery with low power consumption. Second, this approach can be implemented without modifying the C-ToF camera's coding functions, and thus, can be used with a wide range of cameras with minimal changes. We demonstrate the performance benefits of SEC with theoretical analysis, simulations and real experiments, across a wide range of imaging scenarios.

Fluorescence lifetime imaging (FLIM) is used for measuring material properties in a wide range of applications, including biology, medical imaging, chemistry, and material science. In frequency-domain FLIM (FD-FLIM), the object of interest is illuminated with a temporally modulated light source. The fluorescence lifetime is measured by computing the correlations of the emitted light with a demodulation function at the sensor. The signal-to-noise ratio (SNR) and the acquisition time of a FD-FLIM system is determined by the coding scheme (modulation and demodulation functions). In this article, we develop theory and algorithms for designing high-performance FD-FLIM coding schemes that can achieve high SNR and short acquisition time, given a fixed source power budget. Based on a geometric analysis of the image formation and noise model, we propose a novel surrogate objective for the performance of a given coding scheme. The surrogate objective is extremely fast to compute, and can be used to efficiently explore the entire space of coding schemes. Based on this objective, we design novel, high-performance coding schemes that achieve up to an order of magnitude shorter acquisition time as compared to existing approaches. We demonstrate the performance advantage of the proposed schemes in a variety of imaging conditions, using a modular hardware prototype that can implement various coding schemes.