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Dense Variational Reconstruction of Non-rigid Surfaces from Monocular Video
This paper offers the first variational approach to the problem of dense 3D reconstruction of non-rigid surfaces from a monocular video sequence. Expand
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
We propose a unified formulation for the problem of 3D human pose estimation from a single raw RGB image that reasons jointly about 2D joint estimation and 3D pose reconstruction to improve both tasks. Expand
MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects
We present MaskFusion, a real-time, object-aware, semantic and dynamic RGB-D SLAM system that goes beyond traditional systems which output a purely geometric map of a static scene. Expand
Co-fusion: Real-time segmentation, tracking and fusion of multiple objects
  • Martin Rünz, L. Agapito
  • Computer Science, Engineering
  • IEEE International Conference on Robotics and…
  • 1 May 2017
We introduce Co-Fusion, a dense SLAM system that takes a live stream of RGB-D images as input and segments the scene into different objects (using either motion or semantic cues) while simultaneously tracking and reconstructing their 3D shape in real time. Expand
Soft Inextensibility Constraints for Template-Free Non-rigid Reconstruction
In this paper, we exploit an inextensibility prior as a way to better constrain the highly ambiguous problem of non-rigid reconstruction from monocular views. Expand
A Variational Approach to Video Registration with Subspace Constraints
This paper addresses the problem of non-rigid video registration, or the computation of optical flow from a reference frame to each of the subsequent images in a sequence, when the camera views deformable objects. Expand
Self-Calibration of Rotating and Zooming Cameras
In this paper we describe the theory and practice of self-calibration of cameras which are fixed in location and may freely rotate while changing their internal parameters by zooming. Expand
Factorization for non-rigid and articulated structure using metric projections
This paper describes a new algorithm for recovering the 3D shape and motion of deformable and articulated objects purely from uncalibrated 2D image measurements using an iterative factorization approach. Expand
Looking Beyond the Image: Unsupervised Learning for Object Saliency and Detection
We propose a principled probabilistic formulation of object saliency as a sampling problem, which allows us to learn, from a large corpus of unlabelled images, which patches of an image are of the greatest interest and most likely to correspond to an object. Expand
Bilinear Modeling via Augmented Lagrange Multipliers (BALM)
This paper presents a unified approach to solve different bilinear factorization problems in computer vision in the presence of missing data. Expand