Olivier Saurer

Live Metric 3D Reconstruction on Mobile Phones

In this paper, we propose a complete on-device 3D reconstruction pipeline for mobile monocular hand-held devices, which generates dense 3D models with absolute scale on-site while simultaneously supplying the user with real-time interactive feedback. The method fills a gap in current cloud-based mobile reconstruction services as it ensures at capture time that the acquired image set fulfills desired quality and completeness criteria. In contrast to existing systems, the developed framework offers multiple innovative solutions. In particular, we investigate the usability of the available on-device inertial sensors to make the tracking and mapping process more resilient to rapid motions and to estimate the metric scale of the captured scene. Moreover, we propose an efficient and accurate scheme for dense stereo matching which allows to reduce the processing time to interactive speed. We demonstrate the performance of the reconstruction pipeline on multiple challenging indoor and outdoor scenes of different size and depth variability.

Large scale visual geo-localization of images in mountainous terrain

Given a picture taken somewhere in the world, automatic geo-localization of that image is a task that would be extremely useful e.g. for historical and forensic sciences, documentation purposes, organization of the worlds photo material and also intelligence applications. While tremendous progress has been made over the last years in visual location recognition within a single city, localization in natural environments is much more difficult, since vegetation, illumination, seasonal changes make appearance-only approaches impractical. In this work, we target mountainous terrain and use digital elevation models to extract representations for fast visual database lookup. We propose an automated approach for very large scale visual localization that can efficiently exploit visual information contours and geometric constraints consistent orientation at the same time. We validate the system on the scale of a whole country Switzerland, 40 000km2 using a new dataset of more than 200 landscape query pictures with ground truth.

Rolling Shutter Stereo

A huge fraction of cameras used nowadays is based on CMOS sensors with a rolling shutter that exposes the image line by line. For dynamic scenes/cameras this introduces undesired effects like stretch, shear and wobble. It has been shown earlier that rotational shake induced rolling shutter effects in hand-held cell phone capture can be compensated based on an estimate of the camera rotation. In contrast, we analyse the case of significant camera motion, e.g.\ where a bypassing street level capture vehicle uses a rolling shutter camera in a 3D reconstruction framework. The introduced error is depth dependent and cannot be compensated based on camera motion/rotation alone, invalidating also rectification for stereo camera systems. On top, significant lens distortion as often present in wide angle cameras intertwines with rolling shutter effects as it changes the time at which a certain 3D point is seen. We show that naive 3D reconstructions (assuming global shutter) will deliver biased geometry already for very mild assumptions on vehicle speed and resolution. We then develop rolling shutter dense multiview stereo algorithms that solve for time of exposure and depth at the same time, even in the presence of lens distortion and perform an evaluation on ground truth laser scan models as well as on real street-level data.

A minimal solution to the rolling shutter pose estimation problem

Artefacts that are present in images taken from a moving rolling shutter camera degrade the accuracy of absolute pose estimation. To alleviate this problem, we introduce an addition linear velocity in the camera projection matrix to approximate the motion of the rolling shutter camera. In particular, we derive a minimal solution using the Gröbner Basis that solves for the absolute pose as well as the motion of a rolling shutter camera. We show that the minimal problem requires 5-point correspondences and gives up to 8 real solutions. We also show that our formulation can be extended to use more than 5-point correspondences. We use RANSAC to robustly get all the inliers. In the final step, we relax the linear velocity assumption and do a non-linear refinement on the fuli motion, i.e. linear and angular velocities, and pose of the rolling shutter camera with all the inliers. We verify the feasibility and accuracy of our algorithm with both simulated and real-world datasets.

Sparse to Dense 3D Reconstruction from Rolling Shutter Images

It is well known that the rolling shutter effect in images captured with a moving rolling shutter camera causes inaccuracies to 3D reconstructions. The problem is further aggravated with weak visual connectivity from wide baseline images captured with a fast moving camera. In this paper, we propose and implement a pipeline for sparse to dense 3D construction with wide baseline images captured from a fast moving rolling shutter camera. Specifically, we propose a cost function for Bundle Adjustment (BA) that models the rolling shutter effect, incorporates GPS/INS readings, and enforces pairwise smoothness between neighboring poses. We optimize over the 3D structures, camera poses and velocities. We also introduce a novel interpolation scheme for the rolling shutter plane sweep stereo algorithm that allows us to achieve a 7× speed up in the depth map computations for dense reconstruction without losing accuracy. We evaluate our proposed pipeline over a 2.6km image sequence captured with a rolling shutter camera mounted on a moving car.

Leveraging Topographic Maps for Image to Terrain Alignment

With the wide-spread availability of photographic and cartographic data, it becomes desirable to be able to geo-localize any picture in the world. Existing approaches have so far shown impressive results, but they are still lacking in either precision or applicability. In the present work, we explore as an additional cue, semantic image labeling coupled with topographic maps. As an intermediate step towards the ultimate goal of universal geo-localiztion, we show that these cues are suitable for estimating the viewing direction of a terrestrial image, given the images location.

A Homography Formulation to the 3pt Plus a Common Direction Relative Pose Problem

In this paper we present an alternative formulation for the minimal solution to the 3pt plus a common direction relative pose problem. Instead of the commonly used epipolar constraint we use the homography constraint to derive a novel formulation for the 3pt problem. This formulation allows the computation of the normal vector of the plane defined by the three input points without any additional computation in addition to the standard motion parameters of the camera. We show the working of the method on synthetic and real data sets and compare it to the standard 3pt method and the 5pt method for relative pose estimation. In addition we analyze the degenerate conditions for the proposed method.

Exploiting line metric reconstruction from non-central circular panoramas

Comparison among non-central systems for single-view line metric reconstruction.Non-Manhattan line metric reconstruction from single image in non-central panoramas.Automatic line-image extraction in non-central panoramas. In certain non-central imaging systems, straight lines are projected via a non-planar surface encapsulating the 4 degrees of freedom of the 3D line. Consequently the geometry of the 3D line can be recovered from a minimum of four image points. However, with classical non-central catadioptric systems there is not enough effective baseline for a practical implementation of the method. In this paper we propose a multi-camera system configuration resembling the circular panoramic model which results in a particular non-central projection allowing the stitching of a non-central panorama. From a single panorama we obtain well-conditioned 3D reconstruction of lines, which are specially interesting in texture-less scenarios. No previous information about the direction or arrangement of the lines in the scene is assumed. The proposed method is evaluated on both synthetic and real images.

From Point Clouds to Mesh Using Regression

Surface reconstruction from a point cloud is a standard subproblem in many algorithms for dense 3D reconstruction from RGB images or depth maps. Methods, performing only local operations in the vicinity of individual points, are very fast, but reconstructed models typically contain lots of holes. On the other hand, regularized volumetric approaches, formulated as a global optimization, are typically too slow for real-time interactive applications. We propose to use a regression forest based method, which predicts the projection of a grid point to the surface, depending on the spatial configuration of point density in the grid point neighborhood. We designed a suitable feature vector and efficient oct-tree based GPU evaluation, capable of predicting surface of high resolution 3D models in milliseconds. Our method learns and predicts surfaces from an observed point cloud sparser than the evaluation grid, and therefore effectively acts as a regularizer.

Homography Based Egomotion Estimation with a Common Direction

In this paper, we explore the different minimal solutions for egomotion estimation of a camera based on homography knowing the gravity vector between calibrated images. These solutions depend on the prior knowledge about the reference plane used by the homography. We then demonstrate that the number of matched points can vary from two to three and that a direct closed-form solution or a Gröbner basis based solution can be derived according to this plane. Many experimental results on synthetic and real sequences in indoor and outdoor environments show the efficiency and the robustness of our approach compared to standard methods.