Spatial-Temporal Union of Subspaces for Multi-body Non-rigid Structure-from-Motion

05/14/2017
by   Suryansh Kumar, et al.
0

Non-rigid structure-from-motion (NRSfM) has so far been mostly studied for recovering 3D structure of a single non-rigid/deforming object. To handle the real world challenging multiple deforming objects scenarios, existing methods either pre-segment different objects in the scene or treat multiple non-rigid objects as a whole to obtain the 3D non-rigid reconstruction. However, these methods fail to exploit the inherent structure in the problem as the solution of segmentation and the solution of reconstruction could not benefit each other. In this paper, we propose a unified framework to jointly segment and reconstruct multiple non-rigid objects. To compactly represent complex multi-body non-rigid scenes, we propose to exploit the structure of the scenes along both temporal direction and spatial direction, thus achieving a spatio-temporal representation. Specifically, we represent the 3D non-rigid deformations as lying in a union of subspaces along the temporal direction and represent the 3D trajectories as lying in the union of subspaces along the spatial direction. This spatio-temporal representation not only provides competitive 3D reconstruction but also outputs robust segmentation of multiple non-rigid objects. The resultant optimization problem is solved efficiently using the Alternating Direction Method of Multipliers (ADMM). Extensive experimental results on both synthetic and real multi-body NRSfM datasets demonstrate the superior performance of our proposed framework compared with the state-of-the-art methods.

READ FULL TEXT

page 3

page 6

page 16

page 17

page 18

page 19

page 20

page 22

research
06/27/2017

Dense Non-rigid Structure-from-Motion Made Easy - A Spatial-Temporal Smoothness based Solution

This paper proposes a simple spatial-temporal smoothness based method fo...
research
12/03/2021

Class-agnostic Reconstruction of Dynamic Objects from Videos

We introduce REDO, a class-agnostic framework to REconstruct the Dynamic...
research
11/21/2017

Identifying Most Walkable Direction for Navigation in an Outdoor Environment

We present an approach for identifying the most walkable direction for n...
research
03/01/2018

Scalable Dense Non-rigid Structure-from-Motion: A Grassmannian Perspective

This paper addresses the task of dense non-rigid structure from motion (...
research
10/08/2016

4D Crop Monitoring: Spatio-Temporal Reconstruction for Agriculture

Autonomous crop monitoring at high spatial and temporal resolution is a ...
research
06/18/2020

Learning non-rigid surface reconstruction from spatio-temporal image patches

We present a method to reconstruct a dense spatio-temporal depth map of ...
research
07/21/2020

Procrustean Regression Networks: Learning 3D Structure of Non-Rigid Objects from 2D Annotations

We propose a novel framework for training neural networks which is capab...

Please sign up or login with your details

Forgot password? Click here to reset