3,464 research outputs found
Globally-Coordinated Locally-Linear Modeling of Multi-Dimensional Data
This thesis considers the problem of modeling and analysis of continuous, locally-linear, multi-dimensional spatio-temporal data. Our work extends the previously reported theoretical work on the global coordination model to temporal analysis of continuous, multi-dimensional data. We have developed algorithms for time-varying data analysis and used them in full-scale, real-world applications. The applications demonstrated in this thesis include tracking, synthesis, recognitions and retrieval of dynamic objects based on their shape, appearance and motion. The proposed approach in this thesis has advantages over existing approaches to analyzing complex spatio-temporal data. Experiments show that the new modeling features of our approach improve the performance of existing approaches in many applications. In object tracking, our approach is the first one to track nonlinear appearance variations by using low-dimensional representation of the appearance change in globally-coordinated linear subspaces. In dynamic texture synthesis, we are able to model non-stationary dynamic textures, which cannot be handled by any of the existing approaches. In human motion synthesis, we show that realistic synthesis can be performed without using specific transition points, or key frames
GASP : Geometric Association with Surface Patches
A fundamental challenge to sensory processing tasks in perception and
robotics is the problem of obtaining data associations across views. We present
a robust solution for ascertaining potentially dense surface patch (superpixel)
associations, requiring just range information. Our approach involves
decomposition of a view into regularized surface patches. We represent them as
sequences expressing geometry invariantly over their superpixel neighborhoods,
as uniquely consistent partial orderings. We match these representations
through an optimal sequence comparison metric based on the Damerau-Levenshtein
distance - enabling robust association with quadratic complexity (in contrast
to hitherto employed joint matching formulations which are NP-complete). The
approach is able to perform under wide baselines, heavy rotations, partial
overlaps, significant occlusions and sensor noise.
The technique does not require any priors -- motion or otherwise, and does
not make restrictive assumptions on scene structure and sensor movement. It
does not require appearance -- is hence more widely applicable than appearance
reliant methods, and invulnerable to related ambiguities such as textureless or
aliased content. We present promising qualitative and quantitative results
under diverse settings, along with comparatives with popular approaches based
on range as well as RGB-D data.Comment: International Conference on 3D Vision, 201
Geometric Inference with Microlens Arrays
This dissertation explores an alternative to traditional fiducial markers where geometric
information is inferred from the observed position of 3D points seen in an image. We offer an alternative approach which enables geometric inference based on the relative orientation
of markers in an image. We present markers fabricated from microlenses whose appearance
changes depending on the marker\u27s orientation relative to the camera. First, we show how
to manufacture and calibrate chromo-coding lenticular arrays to create a known relationship
between the observed hue and orientation of the array. Second, we use 2 small chromo-coding lenticular arrays to estimate the pose of an object. Third, we use 3 large chromo-coding lenticular arrays to calibrate a camera with a single image. Finally, we create another type of fiducial marker from lenslet arrays that encode orientation with discrete black and white appearances. Collectively, these approaches oer new opportunities for pose estimation and camera calibration that are relevant for robotics, virtual reality, and augmented reality
Dyn-E: Local Appearance Editing of Dynamic Neural Radiance Fields
Recently, the editing of neural radiance fields (NeRFs) has gained
considerable attention, but most prior works focus on static scenes while
research on the appearance editing of dynamic scenes is relatively lacking. In
this paper, we propose a novel framework to edit the local appearance of
dynamic NeRFs by manipulating pixels in a single frame of training video.
Specifically, to locally edit the appearance of dynamic NeRFs while preserving
unedited regions, we introduce a local surface representation of the edited
region, which can be inserted into and rendered along with the original NeRF
and warped to arbitrary other frames through a learned invertible motion
representation network. By employing our method, users without professional
expertise can easily add desired content to the appearance of a dynamic scene.
We extensively evaluate our approach on various scenes and show that our
approach achieves spatially and temporally consistent editing results. Notably,
our approach is versatile and applicable to different variants of dynamic NeRF
representations.Comment: project page: https://dyn-e.github.io
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