Enhanced encoder–decoder architecture for accurate monocular depth estimation - Das, Dabbrata; Das, Argho Deb; Sadaf, Farhan (The Visual computer, 2025)
Estimating depth from a single 2D image is a challenging task due to the lack of stereo or multi-view data, which are typically required for depth perception. In state-of-the-art architectures, the main challenge is to efficiently capture complex objects and fine-grained details, which are often difficult to predict. This paper introduces a novel deep-learning-based approach using an enhanced encoder–decoder architecture, where the Inception-ResNet-v2 model serves as the encoder. This is the first instance of utilizing Inception-ResNet-v2 as an encoder for monocular depth estimation, demonstrating improved performance over previous models. It incorporates multi-scale feature extraction to enhance depth prediction accuracy across various object sizes and distances. We propose a composite loss function comprising depth loss, gradient edge loss, and Structural Similarity Index Measure loss, with fine-tuned weights to optimize the weighted sum, ensuring a balance across different aspects of depth estimation. Experimental results on the KITTI dataset show that our model achieves a significantly faster inference time of 0.019 s, outperforming vision transformers in efficiency while maintaining good accuracy. On the NYU-Depth V2 dataset, the model establishes state-of-the-art performance, with an absolute relative error of 0.064, a root-mean-square error of 0.228, and an accuracy of 89.3% for δ < 1.25. These metrics demonstrate that our model can accurately and efficiently predict depth even in challenging scenarios, providing a practical solution for real-time applications.