Fast Automatic Differentiation for Large Scale Bundle Adjustment
نویسندگان
چکیده
منابع مشابه
Benchmarking Automatic Bundle Adjustment Results
In classical photogrammetry, point observations are manually determined by an operator for performing the bundle adjustment of a sequence of images. In such cases, a comparison of different estimates is usually carried out with respect to the estimated 3D object points. Today, a broad range of automatic methods are available for extracting and matching point features across images, even in the ...
متن کاملLarge-Scale Bundle Adjustment by Parameter Vector Partition
We propose an efficient parallel bundle adjustment (BA) algorithm to refine 3D reconstruction of the large-scale structure from motion (SfM) problem, which uses image collections from Internet. Different from the latest BA techniques that improve efficiency by optimizing the reprojection error function with Conjugate Gradient (CG) methods, we employ the parameter vector partition strategy. More...
متن کاملBundle Adjustment in the Large
We present the design and implementation of a new inexact Newton type algorithm for solving large-scale bundle adjustment problems with tens of thousands of images. We explore the use of Conjugate Gradients for calculating the Newton step and its performance as a function of some simple and computationally efficient preconditioners. We show that the common Schur complement trick is not limited ...
متن کاملAutomatic Differentiation of C++ Codes for Large-Scale Scientific Computing
We discuss computing first derivatives for models based on elements, such as large-scale finite-element PDE discretizations, implemented in the C++ programming language. We use a hybrid technique of automatic differentiation (AD) and manual assembly, with local elementlevel derivatives computed via AD and manually summed into the global derivative. C++ templating and operator overloading work w...
متن کاملComputing Gradients in Large-Scale Optimization Using Automatic Differentiation
The accurate and eecient computation of gradients for partially separable functions is central to the solution of large-scale optimization problems, since these functions are ubiquitous in large-scale problems. We describe two approaches for computing gradients of partially separable functions via automatic diierentiation. In our experiments we employ the ADIFOR (Automatic Diierentiation of For...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2018
ISSN: 2169-3536
DOI: 10.1109/access.2018.2812173