387 research outputs found
Reducing "Structure From Motion": a General Framework for Dynamic Vision - Part 2: Experimental Evaluation
A number of methods have been proposed in the literature for estimating scene-structure and ego-motion from a sequence of images using dynamical models. Although all methods may be derived from a "natural" dynamical model within a unified framework, from an engineering perspective there are a number of trade-offs that lead to different strategies depending upon the specific applications and the goals one is targeting.
Which one is the winning strategy? In this paper we analyze the properties of the dynamical models that originate from each strategy under a variety of experimental conditions. For each model we assess the accuracy of the estimates, their robustness to measurement noise, sensitivity to initial conditions and visual angle, effects of the bas-relief ambiguity and occlusions, dependence upon the number of image measurements and their sampling rate
Recursive estimation of camera motion from uncalibrated image sequences
We describe a method for estimating the motion and structure of a scene from a sequence of images taken with a camera whose geometric calibration parameters are unknown. The scheme is based upon a recursive motion estimation scheme, called the “essential filter”, extended according to the epipolar geometric representation presented by Faugeras, Luong, and Maybank (see Proc. of the ECCV92, vol.588 of LNCS, Springer Verlag, 1992) in order to estimate the calibration parameters as well. The motion estimates can then be fed into any “structure from motion” module that processes motion error, in order to recover the structure of the scene
Reducing “Structure from Motion”: a general framework for dynamic vision. 1. Modeling
The literature on recursive estimation of structure and motion from monocular image sequences comprises a large number of apparently unrelated models and estimation techniques. We propose a framework that allows us to derive and compare all models by following the idea of dynamical system reduction. The “natural” dynamic model, derived from the rigidity constraint and the projection model, is first reduced by explicitly decoupling structure (depth) from motion. Then, implicit decoupling techniques are explored, which consist of imposing that some function of the unknown parameters is held constant. By appropriately choosing such a function, not only can we account for models seen so far in the literature, but we can also derive novel ones
Motion from "X" by Compensating "Y"
This paper analyzes the geometry of the visual motion estimation problem in relation to transformations of the input (images) that stabilize particular output functions such as the motion of a point, a line and a plane in the image. By casting the problem within the popular "epipolar geometry", we provide a common framework for including constraints such as point, line of plane fixation by just considering "slices" of the parameter manifold. The models we provide can be used for estimating motion from a batch using the preferred optimization techniques, or for defining dynamic filters that estimate motion from a causal sequence. We discuss methods for performing the necessary compensation by either controlling the support of the camera or by pre-processing the images. The compensation algorithms may be used also for recursively fitting a plane in 3-D both from point-features or directly from brightness. Conversely, they may be used for estimating motion relative to the plane independent of its parameters
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
Stochastic gradient descent (SGD) is widely believed to perform implicit
regularization when used to train deep neural networks, but the precise manner
in which this occurs has thus far been elusive. We prove that SGD minimizes an
average potential over the posterior distribution of weights along with an
entropic regularization term. This potential is however not the original loss
function in general. So SGD does perform variational inference, but for a
different loss than the one used to compute the gradients. Even more
surprisingly, SGD does not even converge in the classical sense: we show that
the most likely trajectories of SGD for deep networks do not behave like
Brownian motion around critical points. Instead, they resemble closed loops
with deterministic components. We prove that such "out-of-equilibrium" behavior
is a consequence of highly non-isotropic gradient noise in SGD; the covariance
matrix of mini-batch gradients for deep networks has a rank as small as 1% of
its dimension. We provide extensive empirical validation of these claims,
proven in the appendix
Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors
We present a method to infer 3D pose and shape of vehicles from a single
image. To tackle this ill-posed problem, we optimize two-scale projection
consistency between the generated 3D hypotheses and their 2D
pseudo-measurements. Specifically, we use a morphable wireframe model to
generate a fine-scaled representation of vehicle shape and pose. To reduce its
sensitivity to 2D landmarks, we jointly model the 3D bounding box as a coarse
representation which improves robustness. We also integrate three task priors,
including unsupervised monocular depth, a ground plane constraint as well as
vehicle shape priors, with forward projection errors into an overall energy
function.Comment: Proc. of the AAAI, September 201
Depth from Brightness of Moving Images
In this note we describe a method for recursively estimating the depth of a scene from a sequence of images. The input to the estimator are brightness values at a number of locations of a grid in a video image, and the output is the relative (scaled) depth corresponding to each image-point. The estimator is invariant with respect to the motion of the viewer, in the sense that the motion parameters are not part of the state of the estimator and therefore the estimates do not depend on motion as long as there is enough parallax (the translational velocity is nonzero). This scheme is a "direct" version of an other algorithm previously presented by the authors for estimating depth from point-feature correspondence independent of motion
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