论文标题
炸药：具有时间约束的动态本地运动模型，可实时匹配
DynaMiTe: A Dynamic Local Motion Model with Temporal Constraints for Robust Real-Time Feature Matching
论文作者
论文摘要
基于特征的视觉探音计和大满贯方法需要连续图像帧之间准确，快速的对应关系，以实时精确的相机姿势估计。当前功能匹配管道要么仅依赖于特征提取器的描述性功能，要么需要计算复杂的优化方案。我们介绍了轻巧的管道炸药，这对描述符的输入不可知，并利用有效的统计措施来利用时空提示。该方法的理论主链在于特征匹配的概率表述和各自的出于物理动机的约束的研究。动态适应性的本地运动模型将特征组封装在有效的数据结构中。时间约束在时间上传输本地运动模型的信息，从而降低了匹配的搜索空间复杂性。炸药在匹配准确性和摄像头姿势估计方面都以高帧速率，优于最先进的匹配方法，在计算上更有效。
Feature based visual odometry and SLAM methods require accurate and fast correspondence matching between consecutive image frames for precise camera pose estimation in real-time. Current feature matching pipelines either rely solely on the descriptive capabilities of the feature extractor or need computationally complex optimization schemes. We present the lightweight pipeline DynaMiTe, which is agnostic to the descriptor input and leverages spatial-temporal cues with efficient statistical measures. The theoretical backbone of the method lies within a probabilistic formulation of feature matching and the respective study of physically motivated constraints. A dynamically adaptable local motion model encapsulates groups of features in an efficient data structure. Temporal constraints transfer information of the local motion model across time, thus additionally reducing the search space complexity for matching. DynaMiTe achieves superior results both in terms of matching accuracy and camera pose estimation with high frame rates, outperforming state-of-the-art matching methods while being computationally more efficient.
