30 research outputs found
Polarized 3D: High-Quality Depth Sensing with Polarization Cues
Coarse depth maps can be enhanced by using the shape information from polarization cues. We propose a framework to combine surface normals from polarization (hereafter polarization normals) with an aligned depth map. Polarization normals have not been used for depth enhancement before. This is because polarization normals suffer from physics-based artifacts, such as azimuthal ambiguity, refractive distortion and fronto-parallel signal degradation. We propose a framework to overcome these key challenges, allowing the benefits of polarization to be used to enhance depth maps. Our results demonstrate improvement with respect to state-of-the-art 3D reconstruction techniques.Charles Stark Draper Laboratory (Doctoral Fellowship)Singapore. Ministry of Education (Academic Research Foundation MOE2013-T2-1-159)Singapore. National Research Foundation (Singapore University of Technology and Design
MIME: Minority Inclusion for Majority Group Enhancement of AI Performance
Several papers have rightly included minority groups in artificial
intelligence (AI) training data to improve test inference for minority groups
and/or society-at-large. A society-at-large consists of both minority and
majority stakeholders. A common misconception is that minority inclusion does
not increase performance for majority groups alone. In this paper, we make the
surprising finding that including minority samples can improve test error for
the majority group. In other words, minority group inclusion leads to majority
group enhancements (MIME) in performance. A theoretical existence proof of the
MIME effect is presented and found to be consistent with experimental results
on six different datasets. Project webpage:
https://visual.ee.ucla.edu/mime.htm
Resolving Multi-path Interference in Time-of-Flight Imaging via Modulation Frequency Diversity and Sparse Regularization
Time-of-flight (ToF) cameras calculate depth maps by reconstructing phase
shifts of amplitude-modulated signals. For broad illumination or transparent
objects, reflections from multiple scene points can illuminate a given pixel,
giving rise to an erroneous depth map. We report here a sparsity regularized
solution that separates K-interfering components using multiple modulation
frequency measurements. The method maps ToF imaging to the general framework of
spectral estimation theory and has applications in improving depth profiles and
exploiting multiple scattering.Comment: 11 Pages, 4 figures, appeared with minor changes in Optics Letter
Coded time of flight cameras: sparse deconvolution to address multipath interference and recover time profiles
Time of flight cameras produce real-time range maps at a relatively low cost using continuous wave amplitude modulation and demodulation. However, they are geared to measure range (or phase) for a single reflected bounce of light and suffer from systematic errors due to multipath interference.
We re-purpose the conventional time of flight device for a new goal: to recover per-pixel sparse time profiles expressed as a sequence of impulses. With this modification, we show that we can not only address multipath interference but also enable new applications such as recovering depth of near-transparent surfaces, looking through diffusers and creating time-profile movies of sweeping light.
Our key idea is to formulate the forward amplitude modulated light propagation as a convolution with custom codes, record samples by introducing a simple sequence of electronic time delays, and perform sparse deconvolution to recover sequences of Diracs that correspond to multipath returns. Applications to computer vision include ranging of near-transparent objects and subsurface imaging through diffusers. Our low cost prototype may lead to new insights regarding forward and inverse problems in light transport.United States. Defense Advanced Research Projects Agency (DARPA Young Faculty Award)Alfred P. Sloan Foundation (Fellowship)Massachusetts Institute of Technology. Media Laboratory. Camera Culture Grou
Making Thermal Imaging More Equitable and Accurate: Resolving Solar Loading Biases
Thermal cameras and thermal point detectors are used to measure the
temperature of human skin. These are important devices that are used everyday
in clinical and mass screening settings, particularly in an epidemic.
Unfortunately, despite the wide use of thermal sensors, the temperature
estimates from thermal sensors do not work well in uncontrolled scene
conditions. Previous work has studied the effect of wind and other environment
factors on skin temperature, but has not considered the heating effect from
sunlight, which is termed solar loading. Existing device manufacturers
recommend that a subject who has been outdoors in sun re-acclimate to an indoor
environment after a waiting period. The waiting period, up to 30 minutes, is
insufficient for a rapid screening tool. Moreover, the error bias from solar
loading is greater for darker skin tones since melanin absorbs solar radiation.
This paper explores two approaches to address this problem. The first approach
uses transient behavior of cooling to more quickly extrapolate the steady state
temperature. A second approach explores the spatial modulation of solar
loading, to propose single-shot correction with a wide-field thermal camera. A
real world dataset comprising of thermal point, thermal image, subjective, and
objective measurements of melanin is collected with statistical significance
for the effect size observed. The single-shot correction scheme is shown to
eliminate solar loading bias in the time of a typical frame exposure (33ms)