5 research outputs found
Learning Temporal Transformations From Time-Lapse Videos
Based on life-long observations of physical, chemical, and biologic phenomena
in the natural world, humans can often easily picture in their minds what an
object will look like in the future. But, what about computers? In this paper,
we learn computational models of object transformations from time-lapse videos.
In particular, we explore the use of generative models to create depictions of
objects at future times. These models explore several different prediction
tasks: generating a future state given a single depiction of an object,
generating a future state given two depictions of an object at different times,
and generating future states recursively in a recurrent framework. We provide
both qualitative and quantitative evaluations of the generated results, and
also conduct a human evaluation to compare variations of our models.Comment: ECCV201
Computer Vision Based 3D Reconstruction : A Review
3D reconstruction are used in many fields starts from the object reconstruction such as site, and cultural artifacts in both ground and under the sea levels. The scientist are beneficial for these task in order to learn and keep the environment into 3D data due to the extinction. In this paper explained vision setup that is commonly used such as single camera, stereo camera, Kinect / Structured Light/ Time of Flight camera and fusion approach. The prior works also explained how the 3D reconstruction perform in many fields and using various algorithms
PersonNeRF: Personalized Reconstruction from Photo Collections
We present PersonNeRF, a method that takes a collection of photos of a
subject (e.g. Roger Federer) captured across multiple years with arbitrary body
poses and appearances, and enables rendering the subject with arbitrary novel
combinations of viewpoint, body pose, and appearance. PersonNeRF builds a
customized neural volumetric 3D model of the subject that is able to render an
entire space spanned by camera viewpoint, body pose, and appearance. A central
challenge in this task is dealing with sparse observations; a given body pose
is likely only observed by a single viewpoint with a single appearance, and a
given appearance is only observed under a handful of different body poses. We
address this issue by recovering a canonical T-pose neural volumetric
representation of the subject that allows for changing appearance across
different observations, but uses a shared pose-dependent motion field across
all observations. We demonstrate that this approach, along with regularization
of the recovered volumetric geometry to encourage smoothness, is able to
recover a model that renders compelling images from novel combinations of
viewpoint, pose, and appearance from these challenging unstructured photo
collections, outperforming prior work for free-viewpoint human rendering.Comment: Project Page: https://grail.cs.washington.edu/projects/personnerf
Innovative Use and Integration of Remote Sensed Geospatial Data for 3D City Modeling and GIS Urban Applications
Modern remote sensing instruments, mounted on a modern aerial platform and assisted through the use of automated procedures are now capable of acquiring data over a vast area in a short timeframe. Thanks to innovative processing methods and algorithms it is then possible to rapidly deliver results with a high detail and accuracy. The discussed thesis provides a detailed overview, through different case studies and examples, on the evolving complete pipeline required to survey, process, store, integrate, analyze and deliver data in the form of a 3D city model and GIS in the urban environment. A comprehensive 3D city model is, in fact, the necessary multi-disciplinary backbone for the ubiquitous sensors of a Smart City