Abstract
Computer vision algorithms build on 2D images or 3D
videos that capture dynamic events at the millisecond time
scale. However, capturing and analyzing “transient images” at the picosecond scale—i.e., at one trillion frames
per second—reveals unprecedented information about a
scene and light transport within. This is not only crucial
for time-of-flight range imaging, but it also helps further
our understanding of light transport phenomena at a more
fundamental level and potentially allows to revisit many assumptions made in different computer vision algorithms.
In this work, we design and evaluate an imaging system
that builds on single photon avalanche diode (SPAD) sensors to capture multi-path responses with picosecond-scale
active illumination. We develop inverse methods that use
modern approaches to deconvolve and denoise measurements in the presence of Poisson noise, and compute transient images at a higher quality than previously reported.
The small form factor, fast acquisition rates, and relatively
low cost of our system potentially makes transient imaging
more practical for a range of applications.