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Photometric Stereo for Dynamic Surface Orientations.
H.W. Kim, B. Wilburn, m. Ben-Ezra. Proc. European Conference on Computer Vision (ECCV), 2010. This works presents a photometric stereo (shape from active illumination) system and method for computing the time-varying shape of non-rigid objects of unknown and spatially varying materials. The prior art uses time-multiplexed illumination but assumes constant surface normals across several frames, fundamentally limiting the accuracy of the estimated normals. We explicitly account for time-varying surface orientations and show that for unknown Lambertian materials, five images are sufficient to recover surface orientation in one frame. Our optimized systems exploits the physical properties of typical cameras and LEDs to use just three images, and also facilitates frame-to-frame image alignment using standard optical flow methods, despite time-varying illumination. This video shows some examples of the fine shape detail we can capture with this system. |
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Radiometric Calibration Using Temporal Irradiance Mixtures.
B. Wilburn, H, Xu,
Y. Matsushita. Proc. IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 2008. We propose a new method for sampling camera response functions:
temporally mixing two uncalibrated irradiances within a single exposure. Calibration methods
rely on some known relationship between irradiance at the camera image plane and measured pixel
intensities. Prior approaches use a color checker chart with known reflectances, registered images
with different exposure ratios, or even the irradiance distribution along edges in the images. We
show that temporally blending irradiances allows us to densely sample the camera response function
with known relative irradiances. Our first method computes the camera response curve using
temporal mixtures of two pixel intensities on an uncalibrated computer display. The second approach
makes use of temporal irradiance mixtures caused by motion blur. Both methods require only one input
image, although more images can be used for improved robustness to noise or to cover more of the response
curve. We show that our methods compute accurate response functions for a variety of cameras. |
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Stereo Reconstruction with Mixed Pixels Using Adaptice Over-Segmentation.
Y. Taguchi, B. Wilburn,
C.L. Zitnick. Proc. IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 2008. We present an over-segmentation
based, dense stereo estimation algorithm that jointly estimates
segmentation and depth. For mixed pixels on segment boundaries, the
algorithm computes the foreground opacity (alpha), as well as color
and depth for the foreground and background. We model the scene as a
collection of fronto-parallel planar segments and use a generative
model for image formation that handles mixed pixels at segment
boundaries. Our method iteratively updates the segmentation based on
color, depth, and shape constraints using MAP estimation. Given a
segmentation, the depth estimates are updated using belief propagation.
We show that our method is competitive with the state-of-the-art based
on the Middlebury stereo evaluation, and that it overcomes limitations of
traditional segmentation based methods while properly handling mixed
pixels. Z-keying results show the advantages of combining opacity and
depth estimation. |
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Surface Enhancement Using Real-Time Photometric Stereo and Reflectance Transformation.
T. Malzbender, B. Wilburn, D. Gelb, and B. Ambrisco, Proc. Eurographics Symposium on
Rendering 2006. Photometric stereo recovers per-pixel estimates
of surface orientation from images of a surface under varying lighting
conditions. Transforming reflectance based on recovered normal
directions is useful for enhancing the appearance of subtle surface
detail. We present the first system that achieves real-time
photometric stereo and reflectance transformation. We also introduce
new GPU-accelerated normal transformations that amplify shape
detail. Our system allows users in fields such as forensics,
archaeology and dermatology to investigate objects and surfaces by
simply holding them in front of the camera. See this video for a summary of
the work and a demonstration of the system. |
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Synthetic Aperture Focusing Using a Shear-Warp Factorization of the Viewing Transform. Vaibhav Vaish, Gaurav Garg, Eino-Ville Talvala, Emilio Antunez, Bennnett Wilburn, Mark Horowitz, and Marc Levoy.
Proc. Workshop on Advanced 3D
Imaging for Safety and Security (in conjunction with CVPR 2005) Oral
presentation. 2005). This paper analyzes the warps required
for synthetic aperture photography using tilted focal planes and
arbitrary camera configurations. We characterize the warps using a new
rank-1 contraint that lets us focus on any plane, without having to
perform metric calibration of the cameras. We show the advantages of
this method with a real-time implementation using 30 cameras from the
Stanford Multiple Camera Array. This video
shows our results. |
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High
Performance Imaging Using Large Camera Arrays. Bennett Wilburn, Neel
Joshi, Vaibhav Vaish, Eino-Ville Talvala, Emilio Antunez, Adam Barth, Andrew Adams, Mark Horowitz
and Marc Levoy.
(Presented at ACM Siggraph
2005). This paper describes the final 100 camera system. We
also present applications enabled by the array's flexible mounting
system, precise timing control, and processing power at each
camera. These include high-resolution, high-dynamic-range video
capture; spatiotemporal view interpolation (think Matrix Bullet Time
effects, but with virtual camera trajectories chosen after
filming); live, real-time synthetic aperture videography; non-linear
synthetic aperture photography; and hybrid-aperture photography (read
the paper for details!). Here's a video
(70MB Quicktime) showing the system and most of these applications. |
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High
Speed Video Using A Dense Camera Array. Bennett Wilburn, Neel
Joshi, Vaibhav Vaish, Marc Levoy and Mark Horowitz. Presented at CVPR 2004. We create a
1560fps video camera using 52 cameras from our array. Because we
compress data in parallel at each camera, our system can stream
indefinitely at this frame rate, eliminating the need for
triggers. Here's the video
of the popping balloon shown at left. The web page for the paper has
several more videos.
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Using
Plane + Parallax for Calibrating Dense Camera Arrays. Vaibhav
Vaish, Bennett Wilburn and Marc Levoy. Presented at CVPR 2004. We present a method for plane
+ parallax calibration of planar camera arrays. This type of
calibration is useful for light field
rendering and synthetic aperture photography and is simpler and
more robust than full geometric calibration. Synthetic aperture
photography uses many cameras to simulate a single large aperture
camera with a very shallow depth of field. By digitally focussing
beyond partially occluding foreground objects like foliage, we can
blur them away to reveal objects in the background. Here's an example
Quicktime
video.
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