Friday 28 March 2025
The quest for accurate depth estimation has long been a thorn in the side of computer vision researchers. One major challenge lies in the presence of reflective surfaces, which can lead to errors in the estimated depth map. A new approach seeks to overcome this hurdle by leveraging triplet mining and reflection-aware knowledge distillation.
Traditional self-supervised monocular depth estimation methods often struggle with reflective surfaces, as they assume that the color and brightness of a point remain constant across different images. This assumption is violated when a surface reflects light, leading to incorrect training and poor performance on test data. To combat this issue, researchers have turned to distillation techniques, which involve training a student model on top of a pre-trained teacher model.
The proposed method takes a novel approach by introducing a reflection-aware triplet mining loss. This loss function selectively applies the triplet loss to reflective regions, allowing the model to learn from these areas while preserving performance in non-reflective regions. The authors also employ a reflection-aware knowledge distillation technique, which enables the student model to learn from both reflective and non-reflective regions.
The method was evaluated on several popular datasets, including ScanNet and 7-Scenes. Results show significant improvements over traditional self-supervised methods, with the proposed approach achieving better performance even in the presence of reflective surfaces. The authors also demonstrated that their method can be applied to various architectures, including Monodepth2, HRDepth, and MonoViT.
The computational overhead of the proposed method is comparable to traditional self-supervised approaches, making it a viable option for real-world applications. Additionally, the method’s ability to selectively apply the triplet loss to reflective regions reduces the risk of overfitting on non-reflective data.
Qualitative results also demonstrate the effectiveness of the proposed approach. The predicted depth maps show improved accuracy in areas with reflective surfaces, while preserving high-frequency details in non-reflective areas. This is particularly evident in indoor scenes, where the method’s ability to handle reflective surfaces is crucial for accurate depth estimation.
While the proposed method shows promise, it is not without its limitations. For example, it does not address the issue of transparent or mirror objects, and may not generalize well to scenarios where multiple reflection lobes are present. Additionally, the method assumes access to ground truth camera pose during training, which can be a limitation in practice.
Despite these limitations, the proposed approach represents an important step forward in the field of self-supervised monocular depth estimation.
Cite this article: “Reflection-Aware Monocular Depth Estimation with Triplet Mining and Knowledge Distillation”, The Science Archive, 2025.
Monocular Depth Estimation, Reflection-Aware, Triplet Mining, Knowledge Distillation, Self-Supervised Learning, Computer Vision, Reflective Surfaces, Depth Maps, Indoor Scenes, Camera Pose
