This work proposes Set of Centre Active Receptive Fields (SCARF), a real-time velocity-invariant representation that preserves raw events while consistently handling fast motion, stationary scenes, and independently moving objects and achieves state-of-the-art performance in both computational efficiency and representation quality.
Abstract
Event cameras provide low-latency, high temporal resolution perception for real-time vision tasks such as robotics.The novel circuitry (i.e. asynchronous, independent pixels) that enables these advantages also introduces new algorithmic challenges. Velocity-invariant representations alleviate missing observations under slow motion and motion blur under fast motion, but most discard temporal information by converting events into image-like representations. We propose Set of Centre Active Receptive Fields (SCARF), a real-time velocity-invariant representation that preserves raw events while consistently handling fast motion, stationary scenes, and independently moving objects. SCARF achieves state-of-the-art performance in both computational efficiency and representation quality.
This paper introduces a spatio-temporal registration framework to increase accuracy of current state-of-the-art event-by-event flow estimation, while also introducing a twofold algorithm acceleration approach and a real-time implementation strategy to mitigate the impact of computation scaling with event rate.
Zhi-Chao Li, Arren J. Glover, Lorenzo Natale et al.· IEEE Transactions on Pattern...· 0 citations
This work investigates how temporal information can be encoded directly within the event representation, proposing a confidence-normalized continuous multi-timescale representation based on logarithmic B-spline temporal encoding together with a geometry-aware local confidence mechanism that exploits the spatial structu...
Fredrik Lundell, Per-Erik Forssén, Mårten Wadenbäck et al.· 1 citation
EventCV, an open-source and extensible Rust library with OpenCV-style Python bindings that lowers the entry barrier to working with event cameras, is presented and three robotics case studies spanning object detection, on-device model inference, and localization are presented.
Adam D. Hines, Michael Milford, Tobias Fischer· 0 citations
This work establishes a new performance benchmark for classical object discovery in event data, providing a highly scalable, training-free solution for resource-constrained visual perception.
P. G. Shenwai, H. Singh, Sridhar Ravi· 0 citations
This work adopts a masked auto-encoding framework to pre-train a Vision Transformer using a sparsity-aware masking algorithm and token selection mechanism to improve downstream performance and control computational complexity.
E. Norrby, Tomas Wilkinson, P. Nyblom et al.· 0 citations
E-WAVE is presented, a correlation-free framework for high-temporal-resolution (HTR) optical flow estimation from event streams that employs global attention mechanism to model long-range feature dependencies and performs trajectory guided feature warping using B\'ezier curve.
Jia-Le Wu, Xiao-Yang Bai, Hao-Ming Yu et al.· 0 citations
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