2,983 research outputs found
Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset
Scene motion, multiple reflections, and sensor noise introduce artifacts in
the depth reconstruction performed by time-of-flight cameras. We propose a
two-stage, deep-learning approach to address all of these sources of artifacts
simultaneously. We also introduce FLAT, a synthetic dataset of 2000 ToF
measurements that capture all of these nonidealities, and allows to simulate
different camera hardware. Using the Kinect 2 camera as a baseline, we show
improved reconstruction errors over state-of-the-art methods, on both simulated
and real data.Comment: ECCV 201
The Deformable Mirror Demonstration Mission (DeMi) CubeSat: optomechanical design validation and laboratory calibration
Coronagraphs on future space telescopes will require precise wavefront
correction to detect Earth-like exoplanets near their host stars. High-actuator
count microelectromechanical system (MEMS) deformable mirrors provide wavefront
control with low size, weight, and power. The Deformable Mirror Demonstration
Mission (DeMi) payload will demonstrate a 140 actuator MEMS deformable mirror
(DM) with \SI{5.5}{\micro\meter} maximum stroke. We present the flight
optomechanical design, lab tests of the flight wavefront sensor and wavefront
reconstructor, and simulations of closed-loop control of wavefront aberrations.
We also present the compact flight DM controller, capable of driving up to 192
actuator channels at 0-250V with 14-bit resolution. Two embedded Raspberry Pi 3
compute modules are used for task management and wavefront reconstruction. The
spacecraft is a 6U CubeSat (30 cm x 20 cm x 10 cm) and launch is planned for
2019.Comment: 15 pages, 10 figues. Presented at SPIE Astronomical Telescopes +
Instrumentation, Austin, Texas, US
Evaluation of CNN-based Single-Image Depth Estimation Methods
While an increasing interest in deep models for single-image depth estimation
methods can be observed, established schemes for their evaluation are still
limited. We propose a set of novel quality criteria, allowing for a more
detailed analysis by focusing on specific characteristics of depth maps. In
particular, we address the preservation of edges and planar regions, depth
consistency, and absolute distance accuracy. In order to employ these metrics
to evaluate and compare state-of-the-art single-image depth estimation
approaches, we provide a new high-quality RGB-D dataset. We used a DSLR camera
together with a laser scanner to acquire high-resolution images and highly
accurate depth maps. Experimental results show the validity of our proposed
evaluation protocol
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