MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation

TL;DR

MAMo introduces a memory and attention framework that enhances monocular video depth estimation by leveraging temporal information, leading to more accurate and efficient depth predictions across various benchmarks.

Contribution

The paper presents a novel memory and attention-based approach that can augment existing single-image depth networks for improved video depth estimation.

Findings

Achieves state-of-the-art accuracy on KITTI, NYU-Depth V2, and DDAD benchmarks.

Improves depth estimation accuracy with lower latency compared to cost-volume methods.

Effectively leverages temporal information through memory and attention mechanisms.

Abstract

We propose MAMo, a novel memory and attention frame-work for monocular video depth estimation. MAMo can augment and improve any single-image depth estimation networks into video depth estimation models, enabling them to take advantage of the temporal information to predict more accurate depth. In MAMo, we augment model with memory which aids the depth prediction as the model streams through the video. Specifically, the memory stores learned visual and displacement tokens of the previous time instances. This allows the depth network to cross-reference relevant features from the past when predicting depth on the current frame. We introduce a novel scheme to continuously update the memory, optimizing it to keep tokens that correspond with both the past and the present visual information. We adopt attention-based approach to process memory features where we first learn the spatio-temporal relation among the resultant visual and displacement memory tokens using self-attention module. Further, the output features of self-attention are aggregated with the current visual features through cross-attention. The cross-attended features are finally given to a decoder to predict depth on the current frame. Through extensive experiments on several benchmarks, including KITTI, NYU-Depth V2, and DDAD, we show that MAMo consistently improves monocular depth estimation networks and sets new state-of-the-art (SOTA) accuracy. Notably, our MAMo video depth estimation provides higher accuracy with lower latency, when omparing to SOTA cost-volume-based video depth models.