141 research outputs found
Registration of 3D Point Clouds and Meshes: A Survey From Rigid to Non-Rigid
Three-dimensional surface registration transforms multiple three-dimensional data sets into the same coordinate system so as to align overlapping components of these sets. Recent surveys have covered different aspects of either rigid or nonrigid registration, but seldom discuss them as a whole. Our study serves two purposes: 1) To give a comprehensive survey of both types of registration, focusing on three-dimensional point clouds and meshes and 2) to provide a better understanding of registration from the perspective of data fitting. Registration is closely related to data fitting in which it comprises three core interwoven components: model selection, correspondences and constraints, and optimization. Study of these components 1) provides a basis for comparison of the novelties of different techniques, 2) reveals the similarity of rigid and nonrigid registration in terms of problem representations, and 3) shows how overfitting arises in nonrigid registration and the reasons for increasing interest in intrinsic techniques. We further summarize some practical issues of registration which include initializations and evaluations, and discuss some of our own observations, insights and foreseeable research trends
3DMiner: Discovering Shapes from Large-Scale Unannotated Image Datasets
We present 3DMiner -- a pipeline for mining 3D shapes from challenging
large-scale unannotated image datasets. Unlike other unsupervised 3D
reconstruction methods, we assume that, within a large-enough dataset, there
must exist images of objects with similar shapes but varying backgrounds,
textures, and viewpoints. Our approach leverages the recent advances in
learning self-supervised image representations to cluster images with
geometrically similar shapes and find common image correspondences between
them. We then exploit these correspondences to obtain rough camera estimates as
initialization for bundle-adjustment. Finally, for every image cluster, we
apply a progressive bundle-adjusting reconstruction method to learn a neural
occupancy field representing the underlying shape. We show that this procedure
is robust to several types of errors introduced in previous steps (e.g., wrong
camera poses, images containing dissimilar shapes, etc.), allowing us to obtain
shape and pose annotations for images in-the-wild. When using images from Pix3D
chairs, our method is capable of producing significantly better results than
state-of-the-art unsupervised 3D reconstruction techniques, both quantitatively
and qualitatively. Furthermore, we show how 3DMiner can be applied to
in-the-wild data by reconstructing shapes present in images from the LAION-5B
dataset. Project Page: https://ttchengab.github.io/3dminerOfficialComment: In ICCV 202
Free-form image registration of human cochlear μCT data using skeleton similarity as anatomical prior
AbstractBetter understanding of the anatomical variability of the human cochlear is important for the design and function of Cochlear Implants. Proper non-rigid alignment of high-resolution cochlear μCT data is a challenge for the typical cubic B-spline registration model. In this paper we study one way of incorporating skeleton-based similarity as an anatomical registration prior. We extract a centerline skeleton of the cochlear spiral, and generate corresponding parametric pseudo-landmarks between samples. These correspondences are included in the cost function of a typical cubic B-spline registration model to provide a more global guidance of the alignment. The resulting registrations are evaluated using different metrics for accuracy and model behavior, and compared to the results of a registration without the prior
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