Mingsong Dou

Mingsong Dou


Ph.D Candidate
Department of Computer Science
University of North Carolina at Chapel Hill
Email: doums at cs.unc.edu

I was a Ph.D student at UNC-Chapel Hill supervised under Professor Henry Fuchs. Before coming to UNC, I received my master degree in Computer Application from the Vision Group at Center for Information Science, Peking University, and bachelor degree in Electronic Engineering from Shandong University

After one year at Microsoft Research, now I work for perceptiveIO Inc.

Recent Projects

Fusion in Motion

Fusion4D: Real-time Performance Capture of Challenging Scenes

Mingsong Dou, Sameh Khamis, Yury Degtyarev, Philip Davidson, Sean Fanello, Adarsh Kowdle, Sergio Orts Escolano, Christoph Rhemann, David Kim, Jonathan Taylor, Pushmeet Kohli, Vladimir Tankovich, Shahram Izadi (SIGGRAPH 2016)

We contribute a new pipeline for live multi-view performance capture, generating temporally coherent high-quality reconstructions in real-time. Our algorithm supports both incremental reconstruction, improving the surface estimation over time, as well as parameterizing the nonrigid scene motion. Our approach is highly robust to both large frame-to-frame motion and topology changes, allowing us to reconstruct extremely challenging scenes. We demonstrate advantages over related real-time techniques that either deform an online generated template or continually fuse depth data nonrigidly into a single reference model. Finally, we show geometric reconstruction results on par with offline methods which require orders of magnitude more processing time and many more RGBD cameras.
[PDF] [VIDEO]

Fusion in Motion

3D Scanning Deformable Objects with a Single RGBD Sensor

Mingsong Dou, Jonathan Taylor, Henry Fuchs, Andrew Fitzgibbon, Shahram Izadi (CVPR 2015)

We present a 3D scanning system for deformable objects using a single RGBD sensor. Our system allows considerable amount of nonrigid deformations during scanning and achieves comparable high quality results. Our system does not use any prior shape knowledge, enabling general object scanning with freeform deformations. To deal with the drift problem when nonrigidly aligning the input sequence, we automatically detect loop closures, distribute the alignment error over the loop, and finally use a bundle adjustment algorithm to optimize for the latent 3D shape and nonrigid deformation parameters simultaneously.
[PDF] [VIDEO]

Room-Sized Reconstruction

Temporally Enhanced 3D Capture of Room-sized Dynamic Scene with Commodity Depth Cameras

Mingsong Dou, Henry Fuchs (IEEE VR2014, Best Short Paper)

In this project, we designed a system to capture the enhanced 3D structure of a room-sized dynamic scene with commodity depth cameras, such as Microsoft Kinects. Our system incorporates temporal information to achieve a noise-free and complete 3D capture of the entire room. More specifically, we pre-scan the static parts of the room offline, and track their movements online. For the dynamic objects, we perform non-rigid alignment between frames and accumulate data over time. Our system also supports the topology changes of the objects and their interactions.
[PDF] [VIDEO]

scanning dynamic objs

Scanning and Tracking Dynamic Objects with Commodity Depth Cameras

Mingsong Dou, Jan-Michael Frahm, Henry Fuchs (ISMAR 2013)

we designed a system that uses commodity depth and color cameras, such as Microsoft Kinects, to fuse the 3D data captured over time for dynamic objects to build a complete and accurate model, and then tracks the model to match later observations. The key ingredients of our system include a nonrigid matching algorithm that aligns 3D observations of dynamic objects by using both geometry and texture measurements, and a volumetric fusion algorithm that fuses noisy 3D data.
[PDF] [VIDEO]

room scan planes

Exploring High-Level Plane Primitives for Indoor 3D Reconstruction with a Hand-held RGB-D Camera

Mingsong Dou, Li Guan, Jan-Michael Frahm, Henry Fuchs (ACCV 2012 WorkShop)

In this project, we propose to extract high level primitives--planes--from an RGB-D camera, in addition to low level image features (e.g., SIFT), to better constrain the problem and help improve indoor 3D reconstruction. More specifically, for frame to frame matching, we propose a new scheme which takes into account both low-level appearance feature correspondences in RGB image and high-level plane correspondences in depth image. In addition, in the global bundle adjustment step, we formulate a novel error measurement that not only takes into account the traditional 3D point re-projection errors, but also the planar surface alignment errors.
[PDF] [VIDEO] [slides]

room sized telepresence

Room-sized Informal Telepresence System

Mingsong Dou, Ying Shi, Jan-Michael Frahm, Henry Fuchs, Bill Mauchly, Mod Marathe (IEEE VR 2012)

We designed a room-sized telepresence system for informal gatherings rather than conventional meetings. Unlike conventional systems which constrain participants to sit in fixed positions, our system aims to facilitate casual conversations between people in two sites. The system consists of a wall of large flat displays at each of the two sites, showing a panorama of the remote scene, constructed from a multiplicity of color and depth cameras. We provided a solution that ameliorates the eye contact problem during conversation in typical scenarios while still maintaining a consistent view of the entire room for all participants. We achieve this by using two sets of cameras–a cluster of ”Panorama Cameras” located at the center of the display wall that are used to capture a panoramic view of the entire room, and a set of ”Personal Cameras” distributed along the display wall to capture front views of nearby participants. A robust segmentation algorithm with the assistance of depth cameras and an image synthesis algorithm work together to generate a consistent view of the entire scene.
[PDF] [VIDEO] [slides]

Last Updated on June 21, 2016