Depth Monocular Estimation with Attention-based Encoder-Decoder Network from Single Image

• URL: http://arxiv.org/abs/2210.13646v1
• Date: Mon, 24 Oct 2022 23:01:25 GMT
• Title: Depth Monocular Estimation with Attention-based Encoder-Decoder Network from Single Image
• Authors: Xin Zhang and Rabab Abdelfattah and Yuqi Song and Samuel A. Dauchert and Xiaofeng wang
• Abstract summary: Vision-based approaches have recently received much attention and can overcome these drawbacks. In this work, we explore an extreme scenario in vision-based settings: estimate a depth map from one monocular image severely plagued by grid artifacts and blurry edges. Our novel approach can find the focus of current image with minimal overhead and avoid losses of depth features.
• Score: 7.753378095194288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Depth information is the foundation of perception, essential for autonomous driving, robotics, and other source-constrained applications. Promptly obtaining accurate and efficient depth information allows for a rapid response in dynamic environments. Sensor-based methods using LIDAR and RADAR obtain high precision at the cost of high power consumption, price, and volume. While due to advances in deep learning, vision-based approaches have recently received much attention and can overcome these drawbacks. In this work, we explore an extreme scenario in vision-based settings: estimate a depth map from one monocular image severely plagued by grid artifacts and blurry edges. To address this scenario, We first design a convolutional attention mechanism block (CAMB) which consists of channel attention and spatial attention sequentially and insert these CAMBs into skip connections. As a result, our novel approach can find the focus of current image with minimal overhead and avoid losses of depth features. Next, by combining the depth value, the gradients of X axis, Y axis and diagonal directions, and the structural similarity index measure (SSIM), we propose our novel loss function. Moreover, we utilize pixel blocks to accelerate the computation of the loss function. Finally, we show, through comprehensive experiments on two large-scale image datasets, i.e. KITTI and NYU-V2, that our method outperforms several representative baselines.

Related papers

• Exploring Deep Learning Image Super-Resolution for Iris Recognition [50.43429968821899]
  We propose the use of two deep learning single-image super-resolution approaches: Stacked Auto-Encoders (SAE) and Convolutional Neural Networks (CNN) We validate the methods with a database of 1.872 near-infrared iris images with quality assessment and recognition experiments showing the superiority of deep learning approaches over the compared algorithms.
  arXiv  Detail & Related papers  (2023-11-02T13:57:48Z)
• Lightweight Monocular Depth Estimation with an Edge Guided Network [34.03711454383413]
  We present a novel lightweight Edge Guided Depth Estimation Network (EGD-Net) In particular, we start out with a lightweight encoder-decoder architecture and embed an edge guidance branch. In order to aggregate the context information and edge attention features, we design a transformer-based feature aggregation module.
  arXiv  Detail & Related papers  (2022-09-29T14:45:47Z)
• Visual Attention-based Self-supervised Absolute Depth Estimation using Geometric Priors in Autonomous Driving [8.045833295463094]
  We introduce a fully Visual Attention-based Depth (VADepth) network, where spatial attention and channel attention are applied to all stages. By continuously extracting the dependencies of features along the spatial and channel dimensions over a long distance, VADepth network can effectively preserve important details. Experimental results on the KITTI dataset show that this architecture achieves the state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-05-18T08:01:38Z)
• End-to-end Learning for Joint Depth and Image Reconstruction from Diffracted Rotation [10.896567381206715]
  We propose a novel end-to-end learning approach for depth from diffracted rotation. Our approach requires a significantly less complex model and less training data, yet it is superior to existing methods in the task of monocular depth estimation.
  arXiv  Detail & Related papers  (2022-04-14T16:14:37Z)
• High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion [84.24973877109181]
  We propose a novel attention-based hierarchical multi-modal fusion network for guided DSR. We show that our approach outperforms state-of-the-art methods in terms of reconstruction accuracy, running speed and memory efficiency.
  arXiv  Detail & Related papers  (2021-04-04T03:28:33Z)
• Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion [56.85837052421469]
  Estimating scene geometry from data obtained with cost-effective sensors is key for robots and self-driving cars. In this paper, we study the problem of predicting dense depth from a single RGB image with optional sparse measurements from low-cost active depth sensors. We introduce Sparse Networks (SANs), a new module enabling monodepth networks to perform both the tasks of depth prediction and completion.
  arXiv  Detail & Related papers  (2021-03-30T21:22:26Z)
• PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View Depth Estimation with Neural Positional Encoding and Distilled Matting Loss [49.66736599668501]
  We propose a self-supervised single-view pixel-level accurate depth estimation network, called PLADE-Net. Our method shows unprecedented accuracy levels, exceeding 95% in terms of the $delta1$ metric on the KITTI dataset.
  arXiv  Detail & Related papers  (2021-03-12T15:54:46Z)
• On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigation [1.9007546108571112]
  Inferring the depth of images is a fundamental inverse problem within the field of Computer Vision. We propose a new lightweight and fast supervised CNN architecture combined with novel feature extraction models. We also introduce an efficient surface normals module, jointly with a simple geometric 2.5D loss function, to solve SIDE problems.
  arXiv  Detail & Related papers  (2020-10-13T18:37:38Z)
• Accurate RGB-D Salient Object Detection via Collaborative Learning [101.82654054191443]
  RGB-D saliency detection shows impressive ability on some challenge scenarios. We propose a novel collaborative learning framework where edge, depth and saliency are leveraged in a more efficient way.
  arXiv  Detail & Related papers  (2020-07-23T04:33:36Z)
• Defocus Blur Detection via Depth Distillation [64.78779830554731]
  We introduce depth information into DBD for the first time. In detail, we learn the defocus blur from ground truth and the depth distilled from a well-trained depth estimation network. Our approach outperforms 11 other state-of-the-art methods on two popular datasets.
  arXiv  Detail & Related papers  (2020-07-16T04:58:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.