Event Location: Newell Simon Hall 1507
Bio: Christopher is a computer vision PhD candidate at the University of Queensland, Australia, with graduation just around the corner. He’s part of the CI2CV Lab led by Prof Simon Lucey. With a degree in Mechatronics Engineering, and having been taught how to work with wood from a young age, he has a very hands-on attitude. While designing and making something – be it physical or software – he’s always considering how someone might approach it, what their first impressions will be, and how this affects their expectations and interactions. By way of design his goal is to manipulate these aspects.

Abstract: Existing vision based solutions for obtaining precise, dense object centric 3D reconstructions require expensive hardware such as high resolution cameras, or integrated depth sensors, while lower resolutions and susceptibility to motion blur and rolling shutter effects have limited the potential of smart devices. With a focus on spatio-angular resolution, this work advances the view that the increasingly ubiquitous high frame rate video capture ability is the key to enabling precise reconstructions using smartphone platforms. This is achieved by making use of both photometric and silhouette based cues.

Recent advances in SLAM (DSO, LSD, and DVO) have demonstrated compelling benefits to using photometric alignment rather than feature based matching such as less drift due to subpixel alignments, and higher robustness to illumination change by explicitly modelling it. All while being fast.

Drawing upon these discoveries, at the core of this work is an entirely photometric object centric bundle adjustment with more focus on the recovery of the object’s surface than on the odometry of the camera. By processing a video in batch, photometric methods can be pushed beyond the limits explored by SLAM algorithms. By exploiting the temporal consistency of high frame rate sequences, reconstructions can be obtained significantly more efficiently than existing batch methods.

Silhouettes are complementary to photometric cues, as they mark the regions where a surface can no longer be directly observed, and allow for the reconstruction of non-lambertian surfaces such as metal and glass. Instead of generating visual hulls by enforcing pixels to be labelled as being inside or outside the object, visual edges are considered as potential occlusion and silhouette boundaries, from which edge clouds with normals are reconstructed. This allows for the reconstruction of boundaries at self-occlusions rather than just foreground-background regions.