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NeuralMeshing: Differentiable Meshing of Implicit Neural Representations
(2022)Lecture Notes in Computer Science ~ Pattern RecognitionThe generation of triangle meshes from point clouds, i.e. meshing, is a core task in computer graphics and computer vision. Traditional techniques directly construct a surface mesh using local decision heuristics, while some recent methods based on neural implicit representations try to leverage data-driven approaches for this meshing process. However, it is challenging to define a learnable representation for triangle meshes of unknown ...Conference Paper -
Camera Pose Estimation using Implicit Distortion Models
(2022)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Low-dimensional parametric models are the de-facto standard in computer vision for intrinsic camera calibration. These models explicitly describe the mapping between incoming viewing rays and image pixels. In this paper, we explore an alternative approach which implicitly models the lens distortion. The main idea is to replace the parametric model with a regularization term that ensures the latent distortion map varies smoothly throughout ...Conference Paper -
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 -
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 -
Learning to Align Sequential Actions in the Wild
(2022)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)State-of-the-art methods for self-supervised sequential action alignment rely on deep networks that find correspondences across videos in time. They either learn frame-to-frame mapping across sequences, which does not leverage temporal information, or assume monotonic alignment between each video pair, which ignores variations in the order of actions. As such, these methods are not able to deal with common real-world scenarios that involve ...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 -
EgoBody: Human Body Shape and Motion of Interacting People from Head-Mounted Devices
(2022)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2022Understanding social interactions from egocentric views is crucial for many applications, ranging from assistive robotics to AR/VR. Key to reasoning about interactions is to understand the body pose and motion of the interaction partner from the egocentric view. However, research in this area is severely hindered by the lack of datasets. Existing datasets are limited in terms of either size, capture/annotation modalities, ground-truth ...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 -
NeFSAC: Neurally Filtered Minimal Samples
(2022)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2022Since RANSAC, a great deal of research has been devoted to improving both its accuracy and run-time. Still, only a few methods aim at recognizing invalid minimal samples early, before the often expensive model estimation and quality calculation are done. To this end, we propose NeFSAC, an efficient algorithm for neural filtering of motion-inconsistent and poorly-conditioned minimal samples. We train NeFSAC to predict the probability of a ...Conference Paper -
CompNVS: Novel View Synthesis with Scene Completion
(2022)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2022We introduce a scalable framework for novel view synthesis from RGB-D images with largely incomplete scene coverage. While generative neural approaches have demonstrated spectacular results on 2D images, they have not yet achieved similar photorealistic results in combination with scene completion where a spatial 3D scene understanding is essential. To this end, we propose a generative pipeline performing on a sparse grid-based neural ...Conference Paper