121904888
Shedding light on stereoscopic segmentation
We propose a variational algorithm to jointly estimate the shape, albedo, and light configuration of a Lambertian scene from a collection of images taken from different vantage points. Our work can be thought of as extending classical multi-view stereo to cases where point correspondence cannot be established, or extending classical shape from shading to the case of multiple views with unknown light sources. We show that a first naive formalization of this problem yields algorithms that are numerically unstable, no matter how close the initialization is to the true geometry. We then propose a computational scheme to overcome this problem, resulting in provably stable algorithms that converge to (local) minima of the cost functional. Although we restrict our attention to Lambertian objects with uniform albedo, extensions of our framework are conceivable.
163013
A Variational Approach to Shape from Defocus
We address the problem of estimating the three-dimensional shape and radiance of a surface in space from images obtained with different focal settings. We pose the problem as an infinite-dimensional optimization and seek for the global shape of the surface by numerically solving a partial differential equation (PDE). Our method has the advantage of being global (so that regularization can be imposed explicitly), efficient (we use level set methods to solve the PDE), and geometrically correct (we do not assume a shift-invariant imaging model, and therefore are not restricted to equifocal surfaces).
7863218
A semi-direct approach to structure from motion
The problem of structure from motion is often decomposed into two steps: feature correspondence and three-dimensional reconstruction. This separation often causes gross errors when establishing correspondence fails. Therefore, we advocate the necessity to integrate visual information not only in time (i.e. across different views), but also in space, by matching regions – rather than points – using explicit photometric deformation models. We present an algorithm that integrates image-feature tracking and three-dimensional motion estimation into a closed loop, while detecting and rejecting outlier regions that do not fit the model. Due to occlusions and the causal nature of our algorithm, a drift in the estimates accumulates over time. We describe a method to perform global registration of local estimates of motion and structure by matching the appearance of feature regions stored over long time periods. We use image intensities to construct a score function that takes into account changes in brightness and contrast. Our algorithm is recursive and suitable for real-time implementation.
9201508
Structure from Motion Causally Integrated Over Time
We describe an algorithm for reconstructing three-dimensional structure and motion causally, in real time from monocular sequences of images. We prove that the algorithm is minimal and stable, in the sense that the estimation error remains bounded with probability one throughout a sequence of arbitrary length. We discuss a scheme for handling occlusions (point features appearing and disappearing) and drift in the scale factor. These issues are crucial for the algorithm to operate in real time on real scenes. We describe in detail the implementation of the algorithm, which runs on a personal computer and has been made available to the community. We report the performance of our implementation on a few representative long sequences of real and synthetic images. The algorithm, which has been tested extensively over the course of the past few years, exhibits honest performance when the scene contains at least 20-40 points with high contrast, when the relative motion is "slow" compared to the sampling frequency of the frame grabber (30 Hz), and the lens aperture is "large enough" (typically more than 30/spl deg/ of visual field).
38046402
A semi-direct approach to structure from motion
Reconstructing three-dimensional structure and motion is often decomposed into two steps: point feature correspondence and three-dimensional reconstruction. This separation often causes gross errors since correspondence relies on the brightness constancy constraint that is local in space and time. Therefore, we advocate the necessity to integrate visual information not only in time (i.e. across different views), but also in space, by matching regions-rather than points-using explicit photometric deformation models. We present an algorithm that integrates 2D region tracking and 3D motion estimation into a closed loop based on an explicit geometric and photometric model, while detecting and rejecting outlier regions that do not fit the model. Our algorithm is recursive and suitable for real-time implementation. Our experiments show that it far exceeds the accuracy and robustness of point feature-based SFM algorithms.
8032236
First story detection in TDT is hard
✁✄✂✆☎✞✝✠✟☛✡✌☞✞✟☛✟✎✍✑✏✓✒✄✔✕✒ ✗✖✘✝✠✡✚✙✛✂✜✍ ✢✂✜✡✜✍✢✝✠✒✤✣✦✥✗✣✧☎★✔✪✩✫✥✬✡✮✭✯✝✠✣✘✰★✱✲✔ ✳✙✴✔✶✵ ✂✌✷✸✂✜✣✯✍☛✹✻✺✘✥✗✟☛✂ ✼☎✽✝✾✣❀✿❁✒✬✩✢❂❃✥✬✍✢✝✠✒✤✣❄✒✗✩✢✰✤✥✗✣✞✝✾❅✌✥✬✍✢✝✠✒✤✣ ❆✍✫✥✗✟☛✭✯✟✌❇❈✍☛✩✫✥✗✡✫✭❀✝✠✣✞✰ ✥✗✣✘☎ ❊❉✞✩✢✟ ❋✍❃✟❋✍✢✒✗✩☛●★☎❀✂✜✍✢✂✌✡✜✍✢✝✠✒✤✣■❍❏✁ ❑✂✆✟☛▲✘✒✼✏ ▼✍✢▲✧✥ ◆✍ ❖✏P▲✘✂✜✣◗✥❘❉✞✩✢✟❋✍ ✟❋✍✢✒✗✩☛●❙☎✞✂✜✍✢✂✌✡✮✍✢✝✾✒✗✣ ❚✟❋●❀✟❋✍✢✂✌❂❯✝✠✟❱✺✘✥✗✟☛✂✼☎❲☞✘✖❳✒✗✣✚✍☛✩✫✥✗✡✫✭❀✝✠✣✞✰❨✍✢✂✌✡✫▲✘✣✞✒✤❩❬✹ ✒✤✰✬●❪❭✤✏✓✂✶✡✌✥✗✣❖✂✜❫❀✖❳✂✌✡✮✍ ❴✍✢▲✘✥✬✍❴✖❳✂✜✩☛✿❵✒✗✩✢❂❃✥✗✣✞✡✌✂✳✏P✝✠❩✠❩✧✺❳✂❛✖❳✒❪✒✗✩✼❍❴✔✳▲✘✥✬✍ ✖✞✩✢✂✌☎✞✝✠✡✜✍✢✝✠✒✤✣❜✝✠✟✕✡✌✒✗✣✘✟☛✝✠✟❋✍✢✂✌✣❪✍ ❝✏P✝❬✍✢▲✎✥✬✡✜✍✢☞✧✥✬❩❪✖❳✂✜✩☛✿❁✒✬✩✢❂❃✥✗✣✞✡✌✂✓✝✠✣❜✔✳✙✴✔ ✂✌✷✗✥✗❩✠☞✧✥◆✍✢✝✠✒✤✣✘✟✌❍❞✁❑✂
✍✢▲✘✂✜✣❢✟☛▲✞✒◆✏ ❣✍✢▲✘✥✬✍❛✍✢✒✎✥✗✡✫▲✘✝✠✂✌✷✸✂
▲✘✝✠✰✤▲❀✹✑❤❪☞✘✥✗❩✠✝❬✍✑● ❉✘✩✢✟❋✍✪✟❋✍✢✒✗✩☛●✴☎✞✂✜✍✢✂✌✡✮✍✢✝✾✒✗✣✪❭◆✍☛✩✫✥✗✡✫✭✯✝✠✣✘✰❛✂✜✐❥✂✌✡✮✍✢✝✾✷✤✂✌✣✘✂✌✟☛✟✪❂✎☞✞✟❋✍✕✝✠❂❦✖✞✩✢✒◆✷✸✂ ✍✢✒✛✥
☎✞✂✌✰✬✩✢✂✌✂✓✍✢▲✘✥✬✍❧✂✜❫❀✖❳✂✜✩✢✝✠✂✌✣✞✡✌✂✳✟☛☞✞✰✤✰✤✂✜✟❋✍✢✟❴✝✠✟❧☞✞✣✘❩✠✝✠✭✸✂✌❩❬●❪❍✪✁❑✂✳✡✌✒✗✣✞✹ ✡✌❩✠☞✧☎❀✂✶✍✢▲✘✥✬✍✓✂✜✐❥✂✌✡✜✍✢✝✠✷✸✂✶❉✘✩✢✟❋✍✳✟❋✍✢✒✬✩☛●❃☎✞✂✮✍✢✂✌✡✜✍✢✝✠✒✤✣❨✝✠✟✓✂✌✝❬✍✢▲✘✂✮✩❛✝✠❂❦✖❳✒✤✟❋✹ ✟☛✝✠✺✘❩✠✂✴✒✗✩P✩✢✂✼❤❪☞✞✝✠✩✢✂✜✟✶✟☛☞✞✺✘✟❋✍✫✥✬✣✯✍✢✝✾✥✬❩✾❩❬●✆☎✞✝❬✐❥✂✜✩✢✂✌✣❪✍✶✥✬✖✘✖✞✩✢✒✤✥✗✡✫▲✘✂✌✟✌❍