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Context-Aware Sequence Alignment using 4D Skeletal Augmentation
(2022)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Temporal alignment of fine-grained human actions in videos is important for numerous applications in computer vision, robotics, and mixed reality. State-of-the-art methods directly learn image-based embedding space by leveraging powerful deep convolutional neural networks. While being straightforward, their results are far from satisfactory, the aligned videos exhibit severe temporal discontinuity without additional post-processing steps. ...Conference Paper -
IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo
(2022)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)We present IterMVS, a new data-driven method for high-resolution multi-view stereo. We propose a novel GRU-based estimator that encodes pixel-wise probability distributions of depth in its hidden state. Ingesting multi-scale matching information, our model refines these distributions over multiple iterations and infers depth and confidence. To extract the depth maps, we combine traditional classification and regression in a novel manner. ...Conference Paper -
NICE-SLAM: Neural Implicit Scalable Encoding for SLAM
(2022)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Neural implicit representations have recently shown encouraging results in various domains, including promising progress in simultaneous localization and mapping (SLAM). Nevertheless, existing methods produce over-smoothed scene reconstructions and have difficulty scaling up to large scenes. These limitations are mainly due to their simple fully-connected network architecture that does not incorporate local information in the observations. ...Conference Paper -
Quantification of Predictive Uncertainty via Inference-Time Sampling
(2022)Lecture Notes in Computer Science ~ Uncertainty for Safe Utilization of Machine Learning in Medical ImagingPredictive variability due to data ambiguities has typically been addressed via construction dedicated models with built-in probabilistic capabilities that are trained to predict uncertainty estimates as variables of interest. These approaches require distinct architectural components and training mechanisms, may include restrictive assumptions and exhibit overconfidence, i.e., high confidence in imprecise predictions. In this work, we ...Conference Paper -
Automatic 3D reconstruction of manifold meshes via delaunay triangulation and mesh sweeping
(2016)2016 IEEE Winter Conference on Applications of Computer Vision (WACV)In this paper we propose a new approach to incrementally initialize a manifold surface for automatic 3D reconstruction from images. More precisely we focus on the automatic initialization of a 3D mesh as close as possible to the final solution; indeed many approaches require a good initial solution for further refinement via multi-view stereo techniques. Our novel algorithm automatically estimates an initial manifold mesh for surface ...Conference Paper -
FMODetect: Robust Detection of Fast Moving Objects
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)We propose the first learning-based approach for fast moving objects detection. Such objects are highly blurred and move over large distances within one video frame. Fast moving objects are associated with a deblurring and matting problem, also called deblatting. We show that the separation of deblatting into consecutive matting and deblurring allows achieving real-time performance, i.e. an order of magnitude speed-up, and thus enabling ...Conference Paper -
Orthographic-Perspective Epipolar Geometry
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)In this paper we consider the epipolar geometry between orthographic and perspective cameras. We generalize many of the classical results for the perspective essential matrix to this setting and derive novel minimal solvers, not only for the calibrated case, but also for partially calibrated and non-central camera setups. While orthographic cameras might seem exotic, they occur naturally in many applications. They can e.g. model 2D maps ...Conference Paper -
Cross-Descriptor Visual Localization and Mapping
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)Visual localization and mapping is the key technology underlying the majority of mixed reality and robotics systems. Most state-of-the-art approaches rely on local features to establish correspondences between images. In this paper, we present three novel scenarios for localization and mapping which require the continuous update of feature representations and the ability to match across different feature types. While localization and ...Conference Paper -
MBA-VO: Motion Blur Aware Visual Odometry
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)Motion blur is one of the major challenges remaining for visual odometry methods. In low-light conditions where longer exposure times are necessary, motion blur can appear even for relatively slow camera motions. In this paper we present a novel hybrid visual odometry pipeline with direct approach that explicitly models and estimates the camera's local trajectory within exposure time. This allows us to actively compensate for any motion ...Conference Paper -
Pixel-Perfect Structure-from-Motion with Featuremetric Refinement
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)Finding local features that are repeatable across multiple views is a cornerstone of sparse 3D reconstruction. The classical image matching paradigm detects keypoints per-image once and for all, which can yield poorly-localized features and propagate large errors to the final geometry. In this paper, we refine two key steps of structure-from-motion by a direct alignment of low-level image information from multiple views: we first adjust ...Conference Paper