In virtually all event-based vision problems, there is the need to select the most recent events, which are assumed to carry the most relevant information content. To achieve this, at least one of three main strategies is applied, namely: 1) constant temporal decay or fixed time window, 2) constant number of events, and 3) flow-based lifetime of events. However, these strategies suffer from at least one major limitation each. We instead propose a novel decay process for event cameras that adapts to the global scene dynamics and whose latency is in the order of nanoseconds. The main idea is to construct an adaptive quantity that encodes the global scene dynamics, denoted by event activity. The proposed method is evaluated in several event-based vision problems and datasets, consistently improving the corresponding baseline methods' performance. We thus believe it can have a significant widespread impact on event-based research. Code available: https://github.com/neuromorphic-paris/event.batch.

Event cameras asynchronously report brightness changes with a temporal resolution in the order of microseconds, which makes them inherently suitable to address problems that involve rapid motion perception. In this paper, we address the problem of time-to-contact (TTC) estimation using a single event camera. This problem is typically addressed by estimating a single global TTC measure, which explicitly assumes that the surface/obstacle is planar and fronto-parallel. We relax this assumption by proposing an incremental event-based method to estimate the TTC that jointly estimates the (up-to scale) inverse depth and global motion using a single event camera. The proposed method is reliable and fast while asynchronously maintaining a TTC map (TTCM), which provides per-pixel TTC estimates. As a side product, the proposed method can also estimate per-event optical flow. We achieve state-of-the-art performances on TTC estimation in terms of accuracy and runtime per event while achieving competitive performance on optical flow estimation.

We propose a novel method that estimates the Kinematic Structure (KS) of arbitrary articulated rigid objects from event-based data. Event cameras are emerging sensors that asynchronously report brightness changes with a time resolution of microseconds, making them suitable candidates for motion-related perception. By assuming that an articulated rigid object is composed of body parts whose shape can be approximately described by a Gaussian distribution, we jointly segment the different parts by combining an adapted Bayesian inference approach and incremental event-based motion estimation. The respective KS is then generated based on the segmented parts and their respective biharmonic distance, which is estimated by building an affinity matrix of points sampled from the estimated Gaussian distributions. The method outperforms frame-based methods in sequences obtained by simulating events from video sequences and achieves a solid performance on new high-speed motions sequences, which frame-based KS estimation methods can not handle.

We propose a novel Dispersion Minimisation framework for event-based vision model estimation, with applications to optical flow and high-speed motion estimation. The framework extends previous event-based motion compensation algorithms by avoiding computing an optimisation score based on an explicit image-based representation, which provides three main benefits: i) The framework can be extended to perform incremental estimation, i.e., on an event-by-event basis. ii) Besides purely visual transformations in 2D, the framework can readily use additional information, e.g., by augmenting the events with depth, to estimate the parameters of motion models in higher dimensional spaces. iii) The optimisation complexity only depends on the number of events. We achieve this by modelling the event alignment according to candidate parameters and minimising the resultant dispersion, which is computed by a family of suitable entropy-based measures. Data whitening is also proposed as a simple and effective pre-processing step to make the framework's accuracy performance more robust, as well as other event-based motion-compensation methods. The framework is evaluated on several challenging motion estimation problems, including 6-DOF transformation, rotational motion, and optical flow estimation, achieving state-of-the-art performance.

This work addresses the problem of 3D kinematic structure learning of arbitrary articulated rigid bodies from RGB-D data sequences. Typically, this problem is addressed by offline methods that process a batch of frames, assuming that complete point trajectories are available. However, this approach is not feasible when considering scenarios that require continuity and fluidity, for instance, human-robot interaction. In contrast, we propose to tackle this problem in an online unsupervised fashion, by recursively maintaining the metric distance of the scene's 3D structure, while achieving real-time performance. The influence of noise is mitigated by building a similarity measure based on a linear embedding representation and incorporating this representation into the original metric distance. The kinematic structure is then estimated based on a combination of implicit motion and spatial properties. The proposed approach achieves competitive performance both quantitatively and qualitatively in terms of estimation accuracy, even compared to offline methods.