Surface Reconstruction from Noisy and Sparse Data

We introduce a set of algorithms for registering, filtering and measuring the similarity of unorganized 3d point clouds, usually obtained from multiple views. We contribute a method for computing the similarity between point clouds that represent closed surfaces, specifically segmented tumors from CT scans. We obtain watertight surfaces and utilize volumetric overlap to determine similarity in a volumetric way. This similarity measure is used to quantify treatment variability based on target volume segmentation both prior to and following radiotherapy planning stages. We also contribute an algorithm for the drift-free registration of thin, non- rigid scans, where drift is the build-up of error caused by sequential pairwise registration, which is the alignment of each scan to its neighbor. We construct an average scan using mutual nearest neighbors, each scan is registered to this average scan, after which we update the average scan and continue this process until convergence. The use case herein is for merging scans of plants from multiple views and registering vascular scans together. Our final contribution is a method for filtering noisy point clouds, specif- ically those constructed from merged depth maps as obtained from a range scanner or multiple view stereo (MVS), applying techniques that have been utilized in finding outliers in clustered data, but not in MVS. We utilize ker- nel density estimation to obtain a probability density function over the space of observed points, utilizing variable bandwidths based on the nature of the neighboring points, Mahalanobis and reachability distances that is more dis- criminative than a classical Mahalanobis distance-based metric

Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction

Accurate recovery of 3D geometrical surfaces from calibrated 2D multi-view images is a fundamental yet active research area in computer vision. Despite the steady progress in multi-view stereo reconstruction, most existing methods are still limited in recovering fine-scale details and sharp features while suppressing noises, and may fail in reconstructing regions with few textures. To address these limitations, this paper presents a Detail-preserving and Content-aware Variational (DCV) multi-view stereo method, which reconstructs the 3D surface by alternating between reprojection error minimization and mesh denoising. In reprojection error minimization, we propose a novel inter-image similarity measure, which is effective to preserve fine-scale details of the reconstructed surface and builds a connection between guided image filtering and image registration. In mesh denoising, we propose a content-aware $\ell_{p}$-minimization algorithm by adaptively estimating the $p$ value and regularization parameters based on the current input. It is much more promising in suppressing noise while preserving sharp features than conventional isotropic mesh smoothing. Experimental results on benchmark datasets demonstrate that our DCV method is capable of recovering more surface details, and obtains cleaner and more accurate reconstructions than state-of-the-art methods. In particular, our method achieves the best results among all published methods on the Middlebury dino ring and dino sparse ring datasets in terms of both completeness and accuracy.Comment: 14 pages,16 figures. Submitted to IEEE Transaction on image processin

Interpreting 16S metagenomic data without clustering to achieve sub-OTU resolution

The standard approach to analyzing 16S tag sequence data, which relies on clustering reads by sequence similarity into Operational Taxonomic Units (OTUs), underexploits the accuracy of modern sequencing technology. We present a clustering-free approach to multi-sample Illumina datasets that can identify independent bacterial subpopulations regardless of the similarity of their 16S tag sequences. Using published data from a longitudinal time-series study of human tongue microbiota, we are able to resolve within standard 97% similarity OTUs up to 20 distinct subpopulations, all ecologically distinct but with 16S tags differing by as little as 1 nucleotide (99.2% similarity). A comparative analysis of oral communities of two cohabiting individuals reveals that most such subpopulations are shared between the two communities at 100% sequence identity, and that dynamical similarity between subpopulations in one host is strongly predictive of dynamical similarity between the same subpopulations in the other host. Our method can also be applied to samples collected in cross-sectional studies and can be used with the 454 sequencing platform. We discuss how the sub-OTU resolution of our approach can provide new insight into factors shaping community assembly.Comment: Updated to match the published version. 12 pages, 5 figures + supplement. Significantly revised for clarity, references added, results not change