Self-supervised multi-frame depth estimation methods only require unlabeled monocular videos for training. However, most existing methods face challenges, including accuracy degradation caused by moving objects in dynamic scenes and scale ambiguity due to the absence of real-world references. In this field, the emergence of low-cost LiDAR sensors highlights the potential to improve the robustness of multi-frame depth estimation by exploiting accurate sparse measurements at the correct scale. Moreover, the LiDAR ranging points often intersect moving objects, providing more precise depth cues for them. This paper explores the impact of few-beam LiDAR data on self-supervised multi-frame depth estimation, proposing a method that fuses multi-frame matching and sparse depth features. It significantly enhances depth estimation robustness, particularly in scenarios involving moving objects and textureless backgrounds. We demonstrate the effectiveness of our approach through comprehensive experiments, showcasing its potential to address the limitations of existing methods and paving the way for more robust and reliable depth estimation based on this paradigm.

Paraphrase generation is a longstanding NLP task and achieves great success with the aid of large corpora.However, transferring a paraphrasing model to another domain encounters the problem of domain shifting especially when the data is sparse.At the same time, widely using large pre-trained language models (PLMs) faces the overfitting problem when training on scarce labeled data.To mitigate these two issues, we propose, LAPA, an effective adapter for PLMs optimized by meta-learning.LAPA has three-stage training on three types of related resources to solve this problem: 1. pre-training PLMs on unsupervised corpora, 2. inserting an adapter layer and meta-training on source domain labeled data, and 3. fine-tuning adapters on a small amount of target domain labeled data.This method enables paraphrase generation models to learn basic language knowledge first, then learn the paraphrasing task itself later, and finally adapt to the target task.Our experimental results demonstrate that LAPA achieves state-of-the-art in supervised, unsupervised, and low-resource settings on three benchmark datasets.With only 2% of trainable parameters and 1% labeled data of the target task, our approach can achieve a competitive performance with previous work.