# Monocular Depth Estimation Based on Dilated Convolutions and Feature Fusion
## 1 Introduction  
The issue of depth estimation in images is a prominent subject within the realm of computer vision [1, 2]. Traditional methods [3] for depth estimation primarily rely on LiDAR technology to acquire depth data. However, this approach is associated with high costs, offers limited depth information, and necessitates stringent environmental conditions [4]. Existing methods for depth estimation are categorized into multi-view depth estimation and monocular depth estimation, depending on the quantity of input images [5]. Binocular depth estimation techniques ascertain depth information through the pairing and alignment of multiple input images, necessitating prior knowledge of camera parameters. The outcomes are significantly impacted by the selection of feature points. Monocular depth estimation [6], which involves predicting depth information from a single input image, is widely preferred for its simplicity. Nevertheless, the estimation results frequently prove unsatisfactory due to the absence of supplementary depth sources and parallax information. In recent years, researchers have started to investigate the application of deep learning methods to tackle the challenge of monocular depth estimation [7], inspired by the remarkable advancements of deep learning technology in areas such as object detection and semantic segmentation. In 2014, [PERSON] et al. [8] introduced a multi-scale depth network aimed at enhancing the precision and robustness of monocular depth estimation. This network aims toforecast global depth information through a coarse-grained network branch, capture local detail information via a fine-grained network branch, and subsequently integrate the outcomes. In 2015, [PERSON] [9] and colleagues introduced a new architecture known as the Deep Convolutional Neural Field (DCNF), which integrates Deep Convolutional Neural Networks (DCNNs) with Conditional Random Fields (CRFs) [10]. This approach does not necessitate the use of geometric priors or any additional information. Instead, it precisely estimates depth information from a single image by acquiring unary and pairwise potentials of the random field within the convolutional neural network. In 2018, [PERSON] et al. [11] introduced the concept of Depth-Ordered Regression. By incorporating a Spacing-Increasing Discretization (SID) strategy for the discretization of depth values, the researchers transformed the training of depth networks into a structured regression task. Atrous Spatial Pyramid Pooling (ASPP) was additionally employed to augment the network's feature extraction capabilities. This approach offers a novel method for monocular depth estimation tasks. In 2020, a method was proposed by [PERSON] et al. [12] to guide depth estimation towards favoring indoor environmental plane structures. Non-local coplanarity constraints were incorporated into the network, along with the utilization of a novel attention mechanism known as Depth Attention Volume (DAV). This approach yielded favorable outcomes on indoor datasets like the NYU Depth Dataset [13] and ScanNet Dataset [14]. In 2023, [PERSON] [15] proposed a simple, efficient, yet powerful framework for dense visual predictions based on the conditional diffusion pipeline. Our approach follows a \"noise-to-map\" generative paradigm for prediction by progressively removing noise from a random Gaussian distribution, guided by the image. The method, called DDP, efficiently extends the denoising diffusion process into the modern perception pipeline. In contrast to conventional monocular image depth estimation algorithms, the methods mentioned above have demonstrated notable success due to the utilization of extensive datasets and the robust feature extraction capabilities inherent in convolutional neural networks. Nevertheless, they encounter challenges related to the unified optimization of models, indistinct boundaries, and incomplete details.