Weighing up the qualities of independence: 21 grams in focus

At the heart (literally) of 21 Grams (2003) is a concern about questions of identity; of measuring that which seems ineffable – the essence of life, or what makes us distinct, unique as individuals, or otherwise. My aim in this paper is to use the film to attempt a similar, if less lofty, enquiry into the current identity of American independent cinema, a sector that often seems equally resistant to being clearly or definitively pinned down and categorized. 21 Grams is a useful exemplar of a number of significant trends in the contemporary indie sector, including both its situation in the industrial landscape and its most distinctive formal qualities. Industrially, the film lies in a position poised between all-out independence and attachment to the empires of the Hollywood major studio-distribution operations. Formally, and in the intersection between form and content, it also occupies a something of a hybrid position: alternative in some dimensions, especially its narrative structure and the use of hyper-realistic visual textures, but also more familiar-conventional in others, including a storyline that might otherwise seem closer to the stuff of somewhat implausible melodrama. In each case, I suggest, this can be taken as representative of a significant proportion of the American independent sector more widely

DART: Distribution Aware Retinal Transform for Event-based Cameras

We introduce a generic visual descriptor, termed as distribution aware retinal transform (DART), that encodes the structural context using log-polar grids for event cameras. The DART descriptor is applied to four different problems, namely object classification, tracking, detection and feature matching: (1) The DART features are directly employed as local descriptors in a bag-of-features classification framework and testing is carried out on four standard event-based object datasets (N-MNIST, MNIST-DVS, CIFAR10-DVS, NCaltech-101). (2) Extending the classification system, tracking is demonstrated using two key novelties: (i) For overcoming the low-sample problem for the one-shot learning of a binary classifier, statistical bootstrapping is leveraged with online learning; (ii) To achieve tracker robustness, the scale and rotation equivariance property of the DART descriptors is exploited for the one-shot learning. (3) To solve the long-term object tracking problem, an object detector is designed using the principle of cluster majority voting. The detection scheme is then combined with the tracker to result in a high intersection-over-union score with augmented ground truth annotations on the publicly available event camera dataset. (4) Finally, the event context encoded by DART greatly simplifies the feature correspondence problem, especially for spatio-temporal slices far apart in time, which has not been explicitly tackled in the event-based vision domain.Comment: 12 pages, revision submitted to TPAMI in Nov 201

Robust Photogeometric Localization over Time for Map-Centric Loop Closure

Map-centric SLAM is emerging as an alternative of conventional graph-based SLAM for its accuracy and efficiency in long-term mapping problems. However, in map-centric SLAM, the process of loop closure differs from that of conventional SLAM and the result of incorrect loop closure is more destructive and is not reversible. In this paper, we present a tightly coupled photogeometric metric localization for the loop closure problem in map-centric SLAM. In particular, our method combines complementary constraints from LiDAR and camera sensors, and validates loop closure candidates with sequential observations. The proposed method provides a visual evidence-based outlier rejection where failures caused by either place recognition or localization outliers can be effectively removed. We demonstrate the proposed method is not only more accurate than the conventional global ICP methods but is also robust to incorrect initial pose guesses.Comment: To Appear in IEEE ROBOTICS AND AUTOMATION LETTERS, ACCEPTED JANUARY 201

The hand that turns the handle: camera operators and the poetics of the camera in pre-revolutionary Russian film

This article seeks to chart the evolving nature of the camera operator's function at a time when Russian cinema was facing the challenge of self-definition, not only in relation to other art forms, but also in relation to world cinema. It will challenge the conventional notion of the cameraman as merely a ‘hand that turns the handle’, a technician (if not automaton) who was responsible only for the correct speed of shooting and exposure of the print. If camera operation started out as a rudimentary craft, one that was nevertheless valued because the mechanisms of the cinematograph were little understood, it rapidly became an art-form as the language of silent cinema acquired sophistication. This article will analyse the evolving nature of the relationship between the director and the camera operator, and the development of certain conventions which pertained to the role of the camera and controlled the expression of dramatic ideas in visual form. It will also seek to identify the reasons for a number of major aesthetic shifts which took place in Russian cinema during the period concerned, and the importance of the visual arts—in particular painting and still photography—in determining those shifts

Deep Drone Racing: From Simulation to Reality with Domain Randomization

Dynamically changing environments, unreliable state estimation, and operation under severe resource constraints are fundamental challenges that limit the deployment of small autonomous drones. We address these challenges in the context of autonomous, vision-based drone racing in dynamic environments. A racing drone must traverse a track with possibly moving gates at high speed. We enable this functionality by combining the performance of a state-of-the-art planning and control system with the perceptual awareness of a convolutional neural network (CNN). The resulting modular system is both platform- and domain-independent: it is trained in simulation and deployed on a physical quadrotor without any fine-tuning. The abundance of simulated data, generated via domain randomization, makes our system robust to changes of illumination and gate appearance. To the best of our knowledge, our approach is the first to demonstrate zero-shot sim-to-real transfer on the task of agile drone flight. We extensively test the precision and robustness of our system, both in simulation and on a physical platform, and show significant improvements over the state of the art.Comment: Accepted as a Regular Paper to the IEEE Transactions on Robotics Journal. arXiv admin note: substantial text overlap with arXiv:1806.0854

Refining personal and social presence in virtual meetings

Virtual worlds show promise for conducting meetings and conferences without the need for physical travel. Current experience suggests the major limitation to the more widespread adoption and acceptance of virtual conferences is the failure of existing environments to provide a sense of immersion and engagement, or of ‘being there’. These limitations are largely related to the appearance and control of avatars, and to the absence of means to convey non-verbal cues of facial expression and body language. This paper reports on a study involving the use of a mass-market motion sensor (Kinect™) and the mapping of participant action in the real world to avatar behaviour in the virtual world. This is coupled with full-motion video representation of participant’s faces on their avatars to resolve both identity and facial expression issues. The outcomes of a small-group trial meeting based on this technology show a very positive reaction from participants, and the potential for further exploration of these concepts