Collaborative Large-Scale Dense 3D Reconstruction with Online Inter-Agent Pose Optimisation
Stuart Golodetz*, Tommaso Cavallari*, Nicholas A. Lord*, Victor A. Prisacariu, David W. Murray,
Philip H. S. Torr

Reconstructing dense, volumetric models of real-world 3D scenes is important for many tasks, but capturing large scenes can take significant time, and the risk of transient changes to the scene goes up as the capture time increases. These are good reasons to want instead to capture several smaller sub-scenes that can be joined to make the whole scene. Achieving this has traditionally been difficult: joining sub-scenes that may never have been viewed from the same angle requires a high-quality relocaliser that can cope with novel poses, and tracking drift in each sub-scene can prevent them from being joined to make a consistent overall scene. Recent advances in mobile hardware, however, have significantly improved our ability to capture medium-sized sub-scenes with little to no tracking drift. Moreover, high-quality regression forest-based relocalisers have recently been made more practical by the introduction of a method to allow them to be trained and used online. In this paper, we leverage these advances to present what to our knowledge is the first system to allow multiple users to collaborate interactively to reconstruct dense, voxel-based models of whole buildings. Using our system, an entire house or lab can be captured and reconstructed in under half an hour using only consumer-grade hardware.

On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation
Tommaso Cavallari, Stuart Golodetz*, Nicholas A. Lord*, Julien Valentin, Luigi Di Stefano,
Philip H. S. Torr

Camera relocalisation is a key problem in computer vision, with applications as diverse as simultaneous localisation and mapping, virtual/augmented reality and navigation. Common techniques either match the current image against keyframes with known poses coming from a tracker, or establish 2D-to-3D correspondences between keypoints in the current image and points in the scene in order to estimate the camera pose. Recently, regression forests have become a popular alternative to establish such correspondences. They achieve accurate results, but must be trained offline on the target scene, preventing relocalisation in new environments. In this paper, we show how to circumvent this limitation by adapting a pre-trained forest to a new scene on the fly. Our adapted forests achieve relocalisation performance that is on par with that of offline forests, and our approach runs in under 150ms, making it desirable for real-time systems that require online relocalisation.

Straight To Shapes
Saumya Jetley*, Michael Sapienza*, Stuart Golodetz, Philip H. S. Torr

Current object detection approaches predict bounding boxes, but these provide little instance-specific information beyond location, scale and aspect ratio. In this work, we propose to directly regress to objects' shapes in addition to their bounding boxes and categories. It is crucial to find an appropriate shape representation that is compact and decodable, and in which objects can be compared for higher-order concepts such as view similarity, pose variation and occlusion. To achieve this, we use a denoising convolutional auto-encoder to establish an embedding space, and place the decoder after a fast end-to-end network trained to regress directly to the encoded shape vectors. This yields what to the best of our knowledge is the first real-time shape prediction network, running at ~35 FPS on a high-end desktop.

Action Recognition
Harkirat S. Behl, Suman Saha, Gurkirt Singh, Michael Sapienza, Fabio Cuzzolin, Philip H. S. Torr

Human action recognition in challenging video data is becoming an increasingly important research area. Given the growing number of cameras and robots pointing their lenses at humans, the need for automatic recognition of human actions arises, promising Google-style video search and

Struck: Structured Output Tracking with Kernels (TPAMI 2015 Version)
Sam Hare*, Stuart Golodetz*, Amir Saffari*, Vibhav Vineet, Ming-Ming Cheng, Stephen L. Hicks, Philip H. S. Torr

We present a framework for adaptive visual object tracking based on structured output prediction. By explicitly allowing the output space to express the needs of the tracker, we avoid the need for an intermediate classification step. Our method uses a kernelised structured output support vector machine (SVM), which is learned online to provide adaptive tracking. To allow our tracker to run at high frame rates, we (a) introduce a budgeting mechanism that prevents the unbounded growth in the number of support vectors that would otherwise occur during tracking, and (b) show how to implement tracking on the GPU.

Incremental Dense Semantic Stereo Fusion for Large-Scale Semantic Scene Reconstruction
Vibhav Vineet*, Ondrej Miksik*, Morten Lidegaard, Matthias Nießner, Stuart Golodetz, Victor A. Prisacariu, Olaf Kahler, David W. Murray, Shahram Izadi, Patrick Perez, Philip H. S. Torr

We propose an end-to-end system that can process the data incrementally and perform real-time dense stereo reconstruction and semantic segmentation of unbounded outdoor environments. The system outputs a per-voxel probability distribution instead of a single label (soft predictions are desirable in robotics, as the vision output is usually fed as input into other subsystems). Our system is also able to handle moving objects more effectively than prior approaches by incorporating knowledge of object classes into the reconstruction process. In order to achieve fast test times, we extensively use the computational power of modern GPUs.