Problem
Research questions and friction points this paper is trying to address.
Event Cameras
Velocity Invariant Representations
Temporal Information
An Event Preserving Velocity Invariant Representation for Event Cameras
🤖 AI Summary
为解决事件相机在不同速度下的感知问题，提出了一种实时保持事件的不变速度表示方法SCARF，有效处理快速运动、静止场景及独立移动物体。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
velocity-invariant representation
event camera
real-time
high temporal resolution
SCARF
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