PPMStereo: Pick-and-Play Memory Construction for Consistent Dynamic Stereo Matching
WANG Yun, Qiaole Dong, Yongjian Zhang, Tin Lun Lam, Yanwei Fu, Dapeng Wu, Junjie Hu
Temporally consistent depth estimation from stereo video is critical for real-world applications such as augmented reality, where inconsistent depth estimation disrupts the immersion of users.Despite its importance, this task remains challenging due to the difficulty in modeling long-term temporal consistency in a computationally efficient manner.Previous methods attempt to address this by aggregating spatio-temporal information but face a fundamental trade-off: limited temporal modeling provides only modest gains, whereas capturing long-range dependencies significantly increases computational cost.To address this limitation, we introduce a memory buffer for modeling long-range spatio-temporal consistency while achieving efficient dynamic stereo matching.Inspired by the two-stage decision-making process in humans, we propose a Pick-and-Play Memory (PPM) construction module for dynamic Stereo matching, dubbed as PPMStereo. PPM consists of a pick process that identifies the most relevant frames and a play process that weights the selected frames adaptively for spatio-temporal aggregation.This two-stage collaborative process maintains a compact yet highly informative memory buffer while achieving temporally consistent information aggregation.Extensive experiments validate the effectiveness of PPMStereo, demonstrating state-of-the-art performance in both accuracy and temporal consistency.