no code implementations • 27 Apr 2024 • Rong Zou, Marc Pollefeys, Denys Rozumnyi
We propose a method for object retrieval in images that are affected by motion blur.
1 code implementation • NeurIPS 2023 • Denys Rozumnyi, Stefan Popov, Kevis-Kokitsi Maninis, Matthias Nießner, Vittorio Ferrari
Based on these 2D annotations, we automatically reconstruct 3D plane equations for the structural elements and their spatial extent in the scene, and connect adjacent elements at the appropriate contact edges.
no code implementations • ICCV 2023 • Denys Rozumnyi, Jiri Matas, Marc Pollefeys, Vittorio Ferrari, Martin R. Oswald
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.
no code implementations • ICCV 2023 • Yiming Zhao, Denys Rozumnyi, Jie Song, Otmar Hilliges, Marc Pollefeys, Martin R. Oswald
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.
1 code implementation • CVPR 2022 • Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Marc Pollefeys
We propose a method for jointly estimating the 3D motion, 3D shape, and appearance of highly motion-blurred objects from a video.
1 code implementation • NeurIPS 2021 • Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Marc Pollefeys
We address the novel task of jointly reconstructing the 3D shape, texture, and motion of an object from a single motion-blurred image.
1 code implementation • ICCV 2021 • Denys Rozumnyi, Jiri Matas, Filip Sroubek, Marc Pollefeys, Martin R. Oswald
Compared to other methods, such as deblatting, the inference is of several orders of magnitude faster and allows applications such as real-time fast moving object detection and retrieval in large video collections.
5 code implementations • CVPR 2021 • Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Jiri Matas, Marc Pollefeys
We propose a method that, given a single image with its estimated background, outputs the object's appearance and position in a series of sub-frames as if captured by a high-speed camera (i. e. temporal super-resolution).
Ranked #1 on Video Super-Resolution on Falling Objects
2 code implementations • CVPR 2020 • Denys Rozumnyi, Jan Kotera, Filip Sroubek, Jiri Matas
We propose a novel method that tracks fast moving objects, mainly non-uniform spherical, in full 6 degrees of freedom, estimating simultaneously their 3D motion trajectory, 3D pose and object appearance changes with a time step that is a fraction of the video frame exposure time.
Ranked #1 on Video Super-Resolution on TbD
2 code implementations • 15 Sep 2019 • Denys Rozumnyi, Jan Kotera, Filip Šroubek, Jiří Matas
Tracking by Deblatting stands for solving an inverse problem of deblurring and image matting for tracking motion-blurred objects.
no code implementations • 2 Sep 2019 • Denys Rozumnyi, Ian Cherabier, Marc Pollefeys, Martin R. Oswald
Our method learns sensor or algorithm properties jointly with semantic depth fusion and scene completion and can also be used as an expert system, e. g. to unify the strengths of various photometric stereo algorithms.
3 code implementations • 9 May 2019 • Jan Kotera, Denys Rozumnyi, Filip Šroubek, Jiří Matas
We propose a novel approach called Tracking by Deblatting based on the observation that motion blur is directly related to the intra-frame trajectory of an object.
Ranked #2 on Video Super-Resolution on TbD
no code implementations • 26 Nov 2017 • James Pritts, Denys Rozumnyi, M. Pawan Kumar, Ondrej Chum
This paper proposes an automated method to detect, group and rectify arbitrarily-arranged coplanar repeated elements via energy minimization.
3 code implementations • CVPR 2017 • Denys Rozumnyi, Jan Kotera, Filip Sroubek, Lukas Novotny, Jiri Matas
The notion of a Fast Moving Object (FMO), i. e. an object that moves over a distance exceeding its size within the exposure time, is introduced.