Search
Results
-
Model-free Consensus Maximization for Non-Rigid Shapes
(2018)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2018Many computer vision methods use consensus maximization to relate measurements containing outliers with the correct transformation model. In the context of rigid shapes, this is typically done using Random Sampling and Consensus (RANSAC) by estimating an analytical model that agrees with the largest number of measurements (inliers). However, small parameter models may not be always available. In this paper, we formulate the model-free ...Conference Paper -
Discriminative Learning of Apparel Features
(2015)2015 14th IAPR International Conference on Machine Vision Applications (MVA)Conference Paper -
Incremental Non-Rigid Structure-from-Motion with Unknown Focal Length
(2018)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2018 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XIIIThe perspective camera and the isometric surface prior have recently gathered increased attention for Non-Rigid Structure-from-Motion (NRSfM). Despite the recent progress, several challenges remain, particularly the computational complexity and the unknown camera focal length. In this paper we present a method for incremental Non-Rigid Structure-from-Motion (NRSfM) with the perspective camera model and the isometric surface prior with ...Conference Paper -
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
(2018)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2018 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XIIIThis work addresses the problem of semantic scene understanding under dense fog. Although considerable progress has been made in semantic scene understanding, it is mainly related to clear-weather scenes. Extending recognition methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation ...Conference Paper -
End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners
(2018)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2018Conference Paper -
Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime
(2018)2018 21st International Conference on Intelligent Transportation Systems (ITSC)This work addresses the problem of semantic image segmentation of nighttime scenes. Although considerable progress has been made in semantic image segmentation, it is mainly related to daytime scenarios. This paper proposes a novel method to progressive adapt the semantic models trained on daytime scenes, along with large-scale annotations therein, to nighttime scenes via the bridge of twilight time - the time between dawn and sunrise, ...Conference Paper -
Crossing Nets: Combining GANs and VAEs with a Shared Latent Space for Hand Pose Estimation
(2017)2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)State-of-the-art methods for 3D hand pose estimation from depth images require large amounts of annotated training data. We propose to model the statistical relationships of 3D hand poses and corresponding depth images using two deep generative models with a shared latent space. By design, our architecture allows for learning from unlabeled image data in a semi-supervised manner. Assuming a one-to-one mapping between a pose and a depth ...Conference Paper -
One-Shot Video Object Segmentation
(2017)2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)Conference Paper -
Self-Supervised 3D Hand Pose Estimation Through Training by Fitting
(2019)We present a self-supervision method for 3D hand pose estimation from depth maps. We begin with a neural network initialized with synthesized data and fine-tune it on real but unlabelled depth maps by minimizing a set of datafitting terms. By approximating the hand surface with a set of spheres, we design a differentiable hand renderer to align estimates by comparing the rendered and input depth maps. In addition, we place a set of priors ...Conference Paper -