Depth Estimation From Monocular Images With Enhanced Encoder-Decoder Architecture

• URL: http://arxiv.org/abs/2410.11610v2
• Date: Wed, 16 Oct 2024 07:09:12 GMT
• Title: Depth Estimation From Monocular Images With Enhanced Encoder-Decoder Architecture
• Authors: Dabbrata Das, Argho Deb Das, Farhan Sadaf,
• Abstract summary: This paper introduces a novel deep learning-based approach using an encoder-decoder architecture. The Inception-ResNet-v2 model is utilized as the encoder. Experimental results on the NYU Depth V2 dataset show that our model achieves state-of-the-art performance.
• Score: 0.0
• License:
• Abstract: Estimating depth from a single 2D image is a challenging task because of the need for stereo or multi-view data, which normally provides depth information. This paper deals with this challenge by introducing a novel deep learning-based approach using an encoder-decoder architecture, where the Inception-ResNet-v2 model is utilized as the encoder. According to the available literature, this is the first instance of using Inception-ResNet-v2 as an encoder for monocular depth estimation, illustrating better performance than previous models. The use of Inception-ResNet-v2 enables our model to capture complex objects and fine-grained details effectively that are generally difficult to predict. Besides, our model incorporates multi-scale feature extraction to enhance depth prediction accuracy across different kinds of object sizes and distances. We propose a composite loss function consisting of depth loss, gradient edge loss, and SSIM loss, where the weights are fine-tuned to optimize the weighted sum, ensuring better balance across different aspects of depth estimation. Experimental results on the NYU Depth V2 dataset show that our model achieves state-of-the-art performance, with an ARE of 0.064, RMSE of 0.228, and accuracy ($\delta$ $<1.25$) of 89.3%. These metrics demonstrate that our model effectively predicts depth, even in challenging circumstances, providing a scalable solution for real-world applications in robotics, 3D reconstruction, and augmented reality.

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