Self-supervised monocular depth estimation (SSMDE) has gained attention in the field of deep learning as it estimates depth without requiring ground truth depth maps. This approach typically uses a photometric consistency loss between a synthesized image, generated from the estimated depth, and the original image, thereby reducing the need for extensive dataset acquisition. However, the conventional photometric consistency loss relies on the Lambertian assumption, which often leads to significant errors when dealing with reflective surfaces that deviate from this model. To address this limitation, we propose a novel framework that incorporates intrinsic image decomposition into SSMDE. Our method synergistically trains for both monocular depth estimation and intrinsic image decomposition. The accurate depth estimation facilitates multi-image consistency for intrinsic image decomposition by aligning different view coordinate systems, while the decomposition process identifies reflective areas and excludes corrupted gradients from the depth training process. Furthermore, our framework introduces a pseudo-depth generation and knowledge distillation technique to further enhance the performance of the student model across both reflective and non-reflective surfaces. Comprehensive evaluations on multiple datasets show that our approach significantly outperforms existing SSMDE baselines in depth prediction, especially on reflective surfaces.

In this paper, we introduce Latest Object Memory (LOM), a system for robustly tracking and continuously updating the latest states of objects by explicitly modeling their presence across video frames. LOM enables consistent tracking and accurate identity management across frames, enhancing both performance and reliability through the video segmentation process. Building upon LOM, we present Latest Object Memory Management (LOMM) for temporally consistent video instance segmentation, significantly improving long-term instance tracking. This enables consistent tracking and accurate identity management across frames, enhancing both performance and reliability through the video segmentation process. Moreover, we introduce Decoupled Object Association (DOA), a strategy that separately handles newly appearing and already existing objects. By leveraging our memory system, DOA accurately assigns object indices, improving matching accuracy and ensuring stable identity consistency, even in dynamic scenes where objects frequently appear and disappear. Extensive experiments and ablation studies demonstrate the superiority of our method over traditional approaches, setting a new state-of-the-art in video instance segmentation. Notably, our LOMM achieves an AP score of 54.0 on YouTube-VIS 2022, a dataset known for its challenging long videos. Project page: this https URL.