5,597 research outputs found
Learning to Reconstruct Shapes from Unseen Classes
From a single image, humans are able to perceive the full 3D shape of an
object by exploiting learned shape priors from everyday life. Contemporary
single-image 3D reconstruction algorithms aim to solve this task in a similar
fashion, but often end up with priors that are highly biased by training
classes. Here we present an algorithm, Generalizable Reconstruction (GenRe),
designed to capture more generic, class-agnostic shape priors. We achieve this
with an inference network and training procedure that combine 2.5D
representations of visible surfaces (depth and silhouette), spherical shape
representations of both visible and non-visible surfaces, and 3D voxel-based
representations, in a principled manner that exploits the causal structure of
how 3D shapes give rise to 2D images. Experiments demonstrate that GenRe
performs well on single-view shape reconstruction, and generalizes to diverse
novel objects from categories not seen during training.Comment: NeurIPS 2018 (Oral). The first two authors contributed equally to
this paper. Project page: http://genre.csail.mit.edu
AUTO3D: Novel view synthesis through unsupervisely learned variational viewpoint and global 3D representation
This paper targets on learning-based novel view synthesis from a single or
limited 2D images without the pose supervision. In the viewer-centered
coordinates, we construct an end-to-end trainable conditional variational
framework to disentangle the unsupervisely learned relative-pose/rotation and
implicit global 3D representation (shape, texture and the origin of
viewer-centered coordinates, etc.). The global appearance of the 3D object is
given by several appearance-describing images taken from any number of
viewpoints. Our spatial correlation module extracts a global 3D representation
from the appearance-describing images in a permutation invariant manner. Our
system can achieve implicitly 3D understanding without explicitly 3D
reconstruction. With an unsupervisely learned viewer-centered
relative-pose/rotation code, the decoder can hallucinate the novel view
continuously by sampling the relative-pose in a prior distribution. In various
applications, we demonstrate that our model can achieve comparable or even
better results than pose/3D model-supervised learning-based novel view
synthesis (NVS) methods with any number of input views.Comment: ECCV 202
A fast 3-D object recognition algorithm for the vision system of a special-purpose dexterous manipulator
A fast 3-D object recognition algorithm that can be used as a quick-look subsystem to the vision system for the Special-Purpose Dexterous Manipulator (SPDM) is described. Global features that can be easily computed from range data are used to characterize the images of a viewer-centered model of an object. This algorithm will speed up the processing by eliminating the low level processing whenever possible. It may identify the object, reject a set of bad data in the early stage, or create a better environment for a more powerful algorithm to carry the work further
View-Based Models of 3D Object Recognition and Class-Specific Invariances
This paper describes the main features of a view-based model of object recognition. The model tries to capture general properties to be expected in a biological architecture for object recognition. The basic module is a regularization network in which each of the hidden units is broadly tuned to a specific view of the object to be recognized
Updating the art history curriculum: incorporating virtual and augmented reality technologies to improve interactivity and engagement
Master's Project (M.Ed.) University of Alaska Fairbanks, 2017This project investigates how the art history curricula in higher education can borrow from and incorporate emerging technologies currently being used in art museums. Many art museums are using augmented reality and virtual reality technologies to transform their visitors' experiences into experiences that are interactive and engaging. Art museums have historically offered static visitor experiences, which have been mirrored in the study of art. This project explores the current state of the art history classroom in higher education, which is historically a teacher-centered learning environment and the learning effects of that environment. The project then looks at how art museums are creating visitor-centered learning environments; specifically looking at how they are using reality technologies (virtual and augmented) to transition into digitally interactive learning environments that support various learning theories. Lastly, the project examines the learning benefits of such tools to see what could (and should) be implemented into the art history curricula at the higher education level and provides a sample section of a curriculum demonstrating what that implementation could look like. Art and art history are a crucial part of our culture and being able to successfully engage with it and learn from it enables the spread of our culture through digital means and of digital culture
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