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We present a particle-based algorithm for modeling highly viscous liquids. Using a numerical time-integration of particle acceleration and velocity, we apply external forces to particles and use a convenient organization, the adhesion matrix, to represent forces between different types of liquids and objects. Viscosity is handled by performing a momentum exchange between particle pairs such that momentum is conserved. Volume is maintained by iteratively adjusting particle positions after each time step. We use a two-tiered approach to time stepping that allows particle positions to be updated many times per frame while expensive operations, such as calculating viscosity and adhesion, are done only a few times per frame. The liquid is rendered using an implicit surface polygonization algorithm, and we present an implicit function that convolves the liquid surface with a Gaussian function, yielding a smooth liquid skin.

We present a new method for generating large numbers of accurate point correspondences between two wide baseline images. This is important for structure-from-motion algorithms, which rely on many correct matches to reduce error in the derived geometric structure. Given a small initial correspondence set we iteratively expand the set with nearby points exhibiting strong affine correlation, and then we constrain the set to an epipolar geometry using RANSAC. A key point to our algorithm is to allow a high error tolerance in the constraint, allowing the correspondence set to expand into many areas of an image before applying a lower error tolerance constraint. We show that this method successfully expands a small set of initial matches, and we demonstrate it on a variety of image pairs.

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The extrinsic camera parameters from video stream images can be accurately estimated by tracking features through the image sequence and using these features to compute parameter estimates. The poses for long video sequences have been estimated in this manner. However, the poses of large sets of still images cannot be estimated using the same strategy because wide-baseline correspondences are not as robust as narrow-baseline feature tracks. Moreover, video pose estimation requires a linear or hierarchically-linear ordering on the images to be calibrated, reducing the image matches to the neighboring video frames. We propose a novel generalization to the linear ordering requirement of video pose estimation by computing the Minimum Spanning Tree of the camera adjacency graph and using the tree hierarchy to determine the calibration order for a set of input images. We validate the pose accuracy using an error metric that is functionally independent of the estimation process. Because we do not rely on feature tracking for generating feature correspondences, our method can use internally calibrated wide-or narrow-baseline images as input, and can estimate the camera poses from multiple video streams without special pre-processing to concatenate the streams.

A prerequisite to calibrated camera pose estimation is the construction of a camera neighborhood adjacency graph, a connected graph defining the pose neighbors of the camera set. Pose neighbors to a camera C are images containing sufficient overlap in image content with the image from C that they can be used to correctly estimate the pose of C using structure-from-motion techniques. In a video stream, the camera neighborhood adjacency graph is often a simple connected path; frame poses are only estimated relative to their immediate neighbors.We propose a novel method to build more complex camera adjacency graphs that are suitable for estimating the pose of large numbers of wide-and narrow-baseline images. We employ Content-Based Image Retrieval techniques to identify similar images likely to be graph neighbors. We also develop an optimization to improve graph accuracy that is based on an observation of common camera motions taken when photographing with the intent of structure-frommotion. Our results substantiate the use of our method for determining neighbors for pose estimation.