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A Practical Approach to 3D Scanning in the Presence of Interreflections, Subsurface Scattering and Defocus
Global or indirect illumination effects such as interreflections and subsurface scattering severely degrade the performance of structured light-based 3D scanning. In this paper, we analyze the errors in structured light, caused by both long-range (interreflections) and short-range (subsurface scattering) indirect illumination. The errors depend on the frequency of the projected patterns, and the nature of indirect illumination. In particular, we show that long-range effects cause decoding errors for low-frequency patterns, whereas short-range effects affect high-frequency patterns. Based on this analysis, we present a practical 3D scanning system which works in the presence of a broad range of indirect illumination. First, we design binary structured light patterns that are resilient to individual indirect illumination effects using simple logical operations and tools from combinatorial mathematics. Scenes exhibiting multiple phenomena are handled by combining results from a small ensemble of such patterns. This combination also allows detecting any residual errors that are corrected by acquiring a few additional images. Our methods can be readily incorporated into existing scanning systems without significant overhead in terms of capture time or hardware. We show results for several scenes with complex shape and material properties
Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits
To navigate in an environment safely and autonomously, robots must accurately
estimate where obstacles are and how they move. Instead of using expensive
traditional 3D sensors, we explore the use of a much cheaper, faster, and
higher resolution alternative: programmable light curtains. Light curtains are
a controllable depth sensor that sense only along a surface that the user
selects. We adapt a probabilistic method based on particle filters and
occupancy grids to explicitly estimate the position and velocity of 3D points
in the scene using partial measurements made by light curtains. The central
challenge is to decide where to place the light curtain to accurately perform
this task. We propose multiple curtain placement strategies guided by
maximizing information gain and verifying predicted object locations. Then, we
combine these strategies using an online learning framework. We propose a novel
self-supervised reward function that evaluates the accuracy of current velocity
estimates using future light curtain placements. We use a multi-armed bandit
framework to intelligently switch between placement policies in real time,
outperforming fixed policies. We develop a full-stack navigation system that
uses position and velocity estimates from light curtains for downstream tasks
such as localization, mapping, path-planning, and obstacle avoidance. This work
paves the way for controllable light curtains to accurately, efficiently, and
purposefully perceive and navigate complex and dynamic environments. Project
website: https://siddancha.github.io/projects/active-velocity-estimation/Comment: 9 pages (main paper), 3 pages (references), 9 pages (appendix
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