MAL：用于自监督深度估计的具有时序与蒸馏提示的运动感知损失
MAL: Motion-Aware Loss with Temporal and Distillation Hints for Self-Supervised Depth Estimation
AI总结：
本文提出运动感知损失MAL，通过时序关联和教师-学生蒸馏改进多帧自监督单目深度估计，在KITTI和CityScapes上分别将误差降低最多4.2%和10.8%。
摘要：
深度感知对于广泛的机器人应用至关重要。多帧自监督深度估计方法因其能够利用大规模、无标注的真实世界数据而受到研究关注。然而，自监督方法通常依赖静态场景假设，其性能在动态环境中往往会下降。为解决这一问题，我们提出了运动感知损失（Motion-Aware Loss），它利用连续输入帧之间的时序关系，并在多帧自监督深度估计方法中引入一种新颖的教师网络与学生网络之间的蒸馏方案。具体而言，我们将运动物体的空间位置与输入帧的时序顺序相关联，以消除由物体运动引起的误差。同时，我们增强了多帧方法中原有的蒸馏方案，以更好地利用教师网络中的知识。MAL是一个新颖的即插即用模块，旨在无缝集成到多帧自监督单目深度估计方法中。将MAL加入先前最先进的方法后，在KITTI和CityScapes基准上深度估计误差分别降低了最多4.2%和10.8%。
英文摘要：
Depth perception is crucial for a wide range of robotic applications. Multi-frame self-supervised depth estimation methods have gained research interest due to their ability to leverage large-scale, unlabeled real-world data. However, the self-supervised methods often rely on the assumption of a static scene and their performance tends to degrade in dynamic environments. To address this issue, we present Motion-Aware Loss, which leverages the temporal relation among consecutive input frames and a novel distillation scheme between the teacher and student networks in the multi-frame self-supervised depth estimation methods. Specifically, we associate the spatial locations of moving objects with the temporal order of input frames to eliminate errors induced by object motion. Meanwhile, we enhance the original distillation scheme in multi-frame methods to better exploit the knowledge from a teacher network. MAL is a novel, plug-and-play module designed for seamless integration into multi-frame self-supervised monocular depth estimation methods. Adding MAL into previous state-of-the-art methods leads to a reduction in depth estimation errors by up to 4.2% and 10.8% on KITTI and CityScapes benchmarks, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
