activity
20172020
most citedDense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation

12 citations · 30 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV20204 cited

Dense Non-Rigid Structure from Motion: A Manifold Viewpoint

Suryansh Kumar, Luc Van Gool, Carlos E. P. de Oliveira +3

Non-Rigid Structure-from-Motion (NRSfM) problem aims to recover 3D geometry of a deforming object from its 2D feature correspondences across multiple frames. Classical approaches t…

cs.CV2019

Superpixel Soup: Monocular Dense 3D Reconstruction of a Complex Dynamic Scene

Suryansh Kumar, Yuchao Dai, Hongdong Li

This work addresses the task of dense 3D reconstruction of a complex dynamic scene from images. The prevailing idea to solve this task is composed of a sequence of steps and is dep…

cs.CV201912 cited

Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation

Suryansh Kumar, Ram Srivatsav Ghorakavi, Yuchao Dai +1

Recent geometric methods need reliable estimates of 3D motion parameters to procure accurate dense depth map of a complex dynamic scene from monocular images \cite{kumar2017monocul…

cs.CV2019

Non-Rigid Structure from Motion: Prior-Free Factorization Method Revisited

Suryansh Kumar

A simple prior free factorization algorithm \cite{dai2014simple} is quite often cited work in the field of Non-Rigid Structure from Motion (NRSfM). The benefit of this work lies in…

cs.CV2019

Jumping Manifolds: Geometry Aware Dense Non-Rigid Structure from Motion

Suryansh Kumar

Given dense image feature correspondences of a non-rigidly moving object across multiple frames, this paper proposes an algorithm to estimate its 3D shape for each frame. To solve…

cs.CV201711 cited

Monocular Dense 3D Reconstruction of a Complex Dynamic Scene from Two Perspective Frames

Suryansh Kumar, Yuchao Dai, Hongdong Li

This paper proposes a new approach for monocular dense 3D reconstruction of a complex dynamic scene from two perspective frames. By applying superpixel over-segmentation to the ima…