Search Results for author:
Found 13 papers, 9 papers with code
Estimating Generic 3D Room Structures from 2D Annotations
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.
Tracking by 3D Model Estimation of Unknown Objects in Videos
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.
Human from Blur: Human Pose Tracking from Blurry 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.
Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in Videos
We propose a method for jointly estimating the 3D motion, 3D shape, and appearance of highly motion-blurred objects from a video.
Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects
We address the novel task of jointly reconstructing the 3D shape, texture, and motion of an object from a single motion-blurred image.
FMODetect: Robust Detection of Fast Moving Objects
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.
DeFMO: Deblurring and Shape Recovery of Fast Moving Objects
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
Sub-frame Appearance and 6D Pose Estimation of Fast Moving Objects
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
Non-Causal Tracking by Deblatting
Tracking by Deblatting stands for solving an inverse problem of deblurring and image matting for tracking motion-blurred objects.
Learned Semantic Multi-Sensor Depth Map Fusion
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.
Intra-frame Object Tracking by Deblatting
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
Coplanar Repeats by Energy Minimization
This paper proposes an automated method to detect, group and rectify arbitrarily-arranged coplanar repeated elements via energy minimization.
The World of Fast Moving Objects
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.
