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Active depth sensing achieves robust depth estimation but is usually limited by the sensing range. Naively increas- ing the optical power can improve sensing range but in- duces eye-safety concerns for many applications, including autonomous robots and augmented reality. In this paper, we propose an adaptive active depth sensor that jointly opti- mizes range, power consumption, and eye-safety. The main observation is that we need not project light patterns to the entire scene but only to small regions of interest where depth is necessary for the application and passive stereo depth es- timation fails. We theoretically compare this adaptive sens- ing scheme with other sensing strategies, such as full-frame projection, line scanning, and point scanning. We show that, to achieve the same maximum sensing distance, the proposed method consumes the least power while having the shortest (best) eye-safety distance. We implement this adaptive sensing scheme with two hardware prototypes, one with a phase-only spatial light modulator (SLM) and the other with a micro-electro-mechanical (MEMS) mirror and diffractive optical elements (DOE). Experimental results validate the advantage of our method and demonstrate its capability of acquiring higher quality geometry adaptively. Please see our project website for video results and code: https://btilmon.github.io/e3d.html 

Light-transport represents the complex interactions of light in a scene. Fast, compressed, and accurate light-transport capture for dynamic scenes is an open challenge in vision and graphics. In this paper, we integrate the classical idea of Lissajous sampling with novel control strategies fordynamic light-transport applicationssuch as relighting water drops and seeing around corners. In particular, this paper introduces an improved Lissajous projector hardware design and discusses calibration and capture for a microelectromechanical (MEMS) mirror-based projector. Further, we show progress towards speeding up the hardware-based Lissajous subsampling for dual light transport frames, and investigate interpolation algorithms for recovering back the missing data. Our captured dynamic light transport results show complex light scattering effects for dense angular sampling, and we also show dual non-line-of-sight (NLoS) capture of dynamic scenes. This work is the first step towards adaptive Lissajous control for dynamic light-transport.