Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark

• URL: http://arxiv.org/abs/2108.03830v1
• Date: Mon, 9 Aug 2021 06:24:35 GMT
• Title: Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark
• Authors: Kun Wang, Zhenyu Zhang, Zhiqiang Yan, Xiang Li, Baobei Xu, Jun Li and Jian Yang
• Abstract summary: We introduce Priors-Based Regularization to learn distribution knowledge from unpaired depth maps. We also leverage Mapping-Consistent Image Enhancement module to enhance image visibility and contrast. Our framework achieves remarkable improvements and state-of-the-art results on two nighttime datasets.
• Score: 20.66405067066299
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Monocular depth estimation aims at predicting depth from a single image or video. Recently, self-supervised methods draw much attention, due to their free of depth annotations and impressive performance on several daytime benchmarks, such as KITTI and Cityscapes. However, they produce weird outputs in more challenging nighttime scenarios because of low visibility and varying illuminations, which bring weak textures and break brightness-consistency assumption, respectively. To address these problems, in this paper we propose a novel framework with several improvements: (1) we introduce Priors-Based Regularization to learn distribution knowledge from unpaired depth maps and prevent model from being incorrectly trained; (2) we leverage Mapping-Consistent Image Enhancement module to enhance image visibility and contrast while maintaining brightness consistency; and (3) we present Statistics-Based Mask strategy to tune the number of removed pixels within textureless regions, using dynamic statistics. Experimental results demonstrate the effectiveness of each component. Meanwhile, our framework achieves remarkable improvements and state-of-the-art results on two nighttime datasets.

Related papers

• Night-to-Day Translation via Illumination Degradation Disentanglement [51.77716565167767]
  Night-to-Day translation aims to achieve day-like vision for nighttime scenes. processing night images with complex degradations remains a significant challenge under unpaired conditions. We propose textbfN2D3 to identify different degradation patterns in nighttime images.
  arXiv  Detail & Related papers  (2024-11-21T08:51:32Z)
• Exploring Reliable Matching with Phase Enhancement for Night-time Semantic Segmentation [58.180226179087086]
  We propose a novel end-to-end optimized approach, named NightFormer, tailored for night-time semantic segmentation. Specifically, we design a pixel-level texture enhancement module to acquire texture-aware features hierarchically with phase enhancement and amplified attention. Our proposed method performs favorably against state-of-the-art night-time semantic segmentation methods.
  arXiv  Detail & Related papers  (2024-08-25T13:59:31Z)
• Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation Models [16.792458193160407]
  Self-supervised depth estimation algorithms rely heavily on frame-warping relationships. We introduce an algorithm designed to achieve accurate self-supervised stereo depth estimation focusing on nighttime conditions.
  arXiv  Detail & Related papers  (2024-05-18T03:07:23Z)
• STEPS: Joint Self-supervised Nighttime Image Enhancement and Depth Estimation [12.392842482031558]
  We propose a method that jointly learns a nighttime image enhancer and a depth estimator, without using ground truth for either task. Our method tightly entangles two self-supervised tasks using a newly proposed uncertain pixel masking strategy. We benchmark the method on two established datasets: nuScenes and RobotCar.
  arXiv  Detail & Related papers  (2023-02-02T18:59:47Z)
• When the Sun Goes Down: Repairing Photometric Losses for All-Day Depth Estimation [47.617222712429026]
  We show how to use a combination of three techniques to allow the existing photometric losses to work for both day and nighttime images. First, we introduce a per-pixel neural intensity transformation to compensate for the light changes that occur between successive frames. Second, we predict a per-pixel residual flow map that we use to correct the reprojection correspondences induced by the estimated ego-motion and depth.
  arXiv  Detail & Related papers  (2022-06-28T09:29:55Z)
• Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation [17.066753214406525]
  We propose a domain-separated network for self-supervised depth estimation of all-day images. Our approach achieves state-of-the-art depth estimation results for all-day images on the challenging Oxford RobotCar dataset.
  arXiv  Detail & Related papers  (2021-08-17T13:52:19Z)
• Self-Supervised Monocular Depth Estimation of Untextured Indoor Rotated Scenes [6.316693022958222]
  Self-supervised deep learning methods have leveraged stereo images for training monocular depth estimation. These methods do not match performance of supervised methods on indoor environments with camera rotation. We propose a novel Filled Disparity Loss term that corrects for ambiguity of image reconstruction error loss in textureless regions.
  arXiv  Detail & Related papers  (2021-06-24T12:27:16Z)
• Learning Monocular Dense Depth from Events [53.078665310545745]
  Event cameras produce brightness changes in the form of a stream of asynchronous events instead of intensity frames. Recent learning-based approaches have been applied to event-based data, such as monocular depth prediction. We propose a recurrent architecture to solve this task and show significant improvement over standard feed-forward methods.
  arXiv  Detail & Related papers  (2020-10-16T12:36:23Z)
• Unsupervised Low-light Image Enhancement with Decoupled Networks [103.74355338972123]
  We learn a two-stage GAN-based framework to enhance the real-world low-light images in a fully unsupervised fashion. Our proposed method outperforms the state-of-the-art unsupervised image enhancement methods in terms of both illumination enhancement and noise reduction.
  arXiv  Detail & Related papers  (2020-05-06T13:37:08Z)
• DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation Learning [65.94499390875046]
  DeFeat-Net is an approach to simultaneously learn a cross-domain dense feature representation. Our technique is able to outperform the current state-of-the-art with around 10% reduction in all error measures.
  arXiv  Detail & Related papers  (2020-03-30T13:10:32Z)

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.