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Human from Blur: Human Pose Tracking from Blurry Images
(2024)2023 IEEE/CVF International Conference on Computer Vision (ICCV)We propose a method to estimate 3D human poses from substantially blurred images. The key idea is to tackle the inverse problem of image deblurring by modeling the forward problem with a 3D human model, a texture map, and a sequence of poses to describe human motion. The blurring process is then modeled by a temporal image aggregation step. Using a differentiable renderer, we can solve the inverse problem by backpropagating the pixel-wise ...Conference Paper -
Tracking by 3D Model Estimation of Unknown Objects in Videos
(2024)2023 IEEE/CVF International Conference on Computer Vision (ICCV)Most model-free visual object tracking methods formulate the tracking task as object location estimation given by a 2D segmentation or a bounding box in each video frame. We argue that this representation is limited and instead propose to guide and improve 2D tracking with an explicit object representation, namely the textured 3D shape and 6DoF pose in each video frame. Our representation tackles a complex long-term dense correspondence ...Conference Paper -
The Drunkard’s Odometry: Estimating Camera Motion in Deforming Scenes
(2023)Advances in Neural Information Processing Systems 36Conference Paper -
Tracking by 3D Model Estimation of Unknown Objects in Videos
(2023)2023 IEEE/CVF International Conference on Computer Vision (ICCV)Most model-free visual object tracking methods formulate the tracking task as object location estimation given by a 2D segmentation or a bounding box in each video frame. We argue that this representation is limited and instead propose to guide and improve 2D tracking with an explicit object representation, namely the textured 3D shape and 6DoF pose in each video frame. Our representation tackles a complex long-term dense correspondence ...Conference Paper -
Human from Blur: Human Pose Tracking from Blurry Images
(2023)2023 IEEE/CVF International Conference on Computer Vision (ICCV)We propose a method to estimate 3D human poses from substantially blurred images. The key idea is to tackle the inverse problem of image deblurring by modeling the forward problem with a 3D human model, a texture map, and a sequence of poses to describe human motion. The blurring process is then modeled by a temporal image aggregation step. Using a differentiable renderer, we can solve the inverse problem by backpropagating the pixel-wise ...Conference Paper -
DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients
(2023)2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Line segments are ubiquitous in our human-made world and are increasingly used in vision tasks. They are complementary to feature points thanks to their spatial extent and the structural information they provide. Traditional line detectors based on the image gradient are extremely fast and accurate, but lack robustness in noisy images and challenging conditions. Their learned counterparts are more repeatable and can handle challenging ...Conference Paper -
Learning-based Relational Object Matching Across Views
(2023)2023 IEEE International Conference on Robotics and Automation (ICRA)Intelligent robots require object-level scene understanding to reason about possible tasks and interactions with the environment. Moreover, many perception tasks such as scene reconstruction, image retrieval, or place recognition can benefit from reasoning on the level of objects. While keypoint-based matching can yield strong results for finding correspondences for images with small to medium view point changes, for large view point ...Conference Paper -
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 -
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 -
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