24,438 research outputs found
Multi-view Convolutional Neural Networks for 3D Shape Recognition
A longstanding question in computer vision concerns the representation of 3D
shapes for recognition: should 3D shapes be represented with descriptors
operating on their native 3D formats, such as voxel grid or polygon mesh, or
can they be effectively represented with view-based descriptors? We address
this question in the context of learning to recognize 3D shapes from a
collection of their rendered views on 2D images. We first present a standard
CNN architecture trained to recognize the shapes' rendered views independently
of each other, and show that a 3D shape can be recognized even from a single
view at an accuracy far higher than using state-of-the-art 3D shape
descriptors. Recognition rates further increase when multiple views of the
shapes are provided. In addition, we present a novel CNN architecture that
combines information from multiple views of a 3D shape into a single and
compact shape descriptor offering even better recognition performance. The same
architecture can be applied to accurately recognize human hand-drawn sketches
of shapes. We conclude that a collection of 2D views can be highly informative
for 3D shape recognition and is amenable to emerging CNN architectures and
their derivatives.Comment: v1: Initial version. v2: An updated ModelNet40 training/test split is
used; results with low-rank Mahalanobis metric learning are added. v3 (ICCV
2015): A second camera setup without the upright orientation assumption is
added; some accuracy and mAP numbers are changed slightly because a small
issue in mesh rendering related to specularities is fixe
Information hiding through variance of the parametric orientation underlying a B-rep face
Watermarking technologies have been proposed for many different,types of digital media. However, to this date, no viable watermarking techniques have yet emerged for the high value B-rep (i.e. Boundary Representation) models used in 3D mechanical CAD systems. In this paper, the authors propose a new approach (PO-Watermarking) that subtly changes a model's geometric representation to incorporate a 'transparent' signature. This scheme enables software applications to create fragile, or robust watermarks without changing the size of the file, or shape of the CAD model. Also discussed is the amount of information the proposed method could transparently embed into a B-rep model. The results presented demonstrate the embedding and retrieval of text strings and investigate the robustness of the approach after a variety of transformation and modifications have been carried out on the data
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