DeMoN: Depth and Motion Network for Learning Monocular Stereo

In this paper we formulate structure from motion as a learning problem. We train a convolutional network end-to-end to compute depth and camera motion from successive, unconstrained image pairs. The architecture is composed of multiple stacked encoder-decoder networks, the core part being an iterative network that is able to improve its own predictions. The network estimates not only depth and motion, but additionally surface normals, optical flow between the images and confidence of the matching. A crucial component of the approach is a training loss based on spatial relative differences. Compared to traditional two-frame structure from motion methods, results are more accurate and more robust. In contrast to the popular depth-from-single-image networks, DeMoN learns the concept of matching and, thus, better generalizes to structures not seen during training.Comment: Camera ready version for CVPR 2017. Supplementary material included. Project page: http://lmb.informatik.uni-freiburg.de/people/ummenhof/depthmotionnet

Hallucinating dense optical flow from sparse lidar for autonomous vehicles

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper we propose a novel approach to estimate dense optical flow from sparse lidar data acquired on an autonomous vehicle. This is intended to be used as a drop-in replacement of any image-based optical flow system when images are not reliable due to e.g. adverse weather conditions or at night. In order to infer high resolution 2D flows from discrete range data we devise a three-block architecture of multiscale filters that combines multiple intermediate objectives, both in the lidar and image domain. To train this network we introduce a dataset with approximately 20K lidar samples of the Kitti dataset which we have augmented with a pseudo ground-truth image-based optical flow computed using FlowNet2. We demonstrate the effectiveness of our approach on Kitti, and show that despite using the low-resolution and sparse measurements of the lidar, we can regress dense optical flow maps which are at par with those estimated with image-based methods.Peer ReviewedPostprint (author's final draft

Data Fusion of Objects Using Techniques Such as Laser Scanning, Structured Light and Photogrammetry for Cultural Heritage Applications

In this paper we present a semi-automatic 2D-3D local registration pipeline capable of coloring 3D models obtained from 3D scanners by using uncalibrated images. The proposed pipeline exploits the Structure from Motion (SfM) technique in order to reconstruct a sparse representation of the 3D object and obtain the camera parameters from image feature matches. We then coarsely register the reconstructed 3D model to the scanned one through the Scale Iterative Closest Point (SICP) algorithm. SICP provides the global scale, rotation and translation parameters, using minimal manual user intervention. In the final processing stage, a local registration refinement algorithm optimizes the color projection of the aligned photos on the 3D object removing the blurring/ghosting artefacts introduced due to small inaccuracies during the registration. The proposed pipeline is capable of handling real world cases with a range of characteristics from objects with low level geometric features to complex ones

Volumetric Calibration Refinement using masked back projection and image correlation superposition

This paper deals with a new, reconstruction based, approach of refining a volumetric calibration. The technique is based on a 2D cross-correlation between particle images on the sensor plane with a planar back projection from a tomographic reconstruction in the same sensor plane to determine potential disparities between the initial camera calibration and the measurement. Additive superposition of the correlation maps from different sets or particle images allows reducing the influence of noise and ghost particles such that the systematic errors in the calibration can be corrected. The different sections describe the theory, the principle processing steps and the convergence of the procedure. Furthermore, the concept is proven by simulating the entire process of the measurement chain, with the help of a synthetic comparison. The results show that disparities of over 9 pixels could be corrected to an average of below 0.1 pixels during the refinement steps. Finally, the technique demonstrates it´s potential to measured data, where the numbers of outliers in the raw results are reduced after the volumetric calibration refinement

Confocal microscopy of colloidal particles: towards reliable, optimum coordinates

Over the last decade, the light microscope has become increasingly useful as a quantitative tool for studying colloidal systems. The ability to obtain particle coordinates in bulk samples from micrographs is particularly appealing. In this paper we review and extend methods for optimal image formation of colloidal samples, which is vital for particle coordinates of the highest accuracy, and for extracting the most reliable coordinates from these images. We discuss in depth the accuracy of the coordinates, which is sensitive to the details of the colloidal system and the imaging system. Moreover, this accuracy can vary between particles, particularly in dense systems. We introduce a previously unreported error estimate and use it to develop an iterative method for finding particle coordinates. This individual-particle accuracy assessment also allows comparison between particle locations obtained from different experiments. Though aimed primarily at confocal microscopy studies of colloidal systems, the methods outlined here should transfer readily to many other feature extraction problems, especially where features may overlap one another.Comment: Accepted by Advances in Colloid and Interface Scienc