LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization

• URL: http://arxiv.org/abs/2010.06323v1
• Date: Tue, 13 Oct 2020 12:15:20 GMT
• Title: LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization
• Authors: Lukas von Stumberg, Patrick Wenzel, Nan Yang, Daniel Cremers
• Abstract summary: LM-Reloc is a novel approach for visual relocalization based on direct image alignment. We propose a loss formulation inspired by the classical Levenberg-Marquardt algorithm to train LM-Net.
• Score: 54.77498358487812
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present LM-Reloc -- a novel approach for visual relocalization based on direct image alignment. In contrast to prior works that tackle the problem with a feature-based formulation, the proposed method does not rely on feature matching and RANSAC. Hence, the method can utilize not only corners but any region of the image with gradients. In particular, we propose a loss formulation inspired by the classical Levenberg-Marquardt algorithm to train LM-Net. The learned features significantly improve the robustness of direct image alignment, especially for relocalization across different conditions. To further improve the robustness of LM-Net against large image baselines, we propose a pose estimation network, CorrPoseNet, which regresses the relative pose to bootstrap the direct image alignment. Evaluations on the CARLA and Oxford RobotCar relocalization tracking benchmark show that our approach delivers more accurate results than previous state-of-the-art methods while being comparable in terms of robustness.

Related papers

• Temporal Attention for Cross-View Sequential Image Localization [17.14320442129364]
  This paper introduces a novel approach to enhancing cross-view localization, focusing on the fine-grained, sequential localization of street-view images within a single known satellite image patch. By expanding to sequential image fine-grained localization, our model, equipped with a novel Temporal Attention Module (TAM), leverages contextual information to significantly improve sequential image localization accuracy.
  arXiv  Detail & Related papers  (2024-08-28T06:53:08Z)
• Bayesian Image Super-Resolution with Deep Modeling of Image Statistics [18.55701190218365]
  We propose a Bayesian image restoration framework, where natural image statistics are modeled with the combination of smoothness and sparsity priors. We develop a variational Bayesian approach to infer their posteriors, and propose an unsupervised training strategy. Experiments on three image restoration tasks, textiti.e., ideal SISR, realistic SISR, and real-world SISR, demonstrate that our method has superior model generalizability against varying noise levels and degradation kernels.
  arXiv  Detail & Related papers  (2022-03-31T20:52:59Z)
• Poseur: Direct Human Pose Regression with Transformers [119.79232258661995]
  We propose a direct, regression-based approach to 2D human pose estimation from single images. Our framework is end-to-end differentiable, and naturally learns to exploit the dependencies between keypoints. Ours is the first regression-based approach to perform favorably compared to the best heatmap-based pose estimation methods.
  arXiv  Detail & Related papers  (2022-01-19T04:31:57Z)
• Global and Local Alignment Networks for Unpaired Image-to-Image Translation [170.08142745705575]
  The goal of unpaired image-to-image translation is to produce an output image reflecting the target domain's style. Due to the lack of attention to the content change in existing methods, semantic information from source images suffers from degradation during translation. We introduce a novel approach, Global and Local Alignment Networks (GLA-Net) Our method effectively generates sharper and more realistic images than existing approaches.
  arXiv  Detail & Related papers  (2021-11-19T18:01:54Z)
• On the Limits of Pseudo Ground Truth in Visual Camera Re-localisation [83.29404673257328]
  Re-localisation benchmarks measure how well each method replicates the results of a reference algorithm. This begs the question whether the choice of the reference algorithm favours a certain family of re-localisation methods. This paper analyzes two widely used re-localisation datasets and shows that evaluation outcomes indeed vary with the choice of the reference algorithm.
  arXiv  Detail & Related papers  (2021-09-01T12:01:08Z)
• Deep Amended Gradient Descent for Efficient Spectral Reconstruction from Single RGB Images [42.26124628784883]
  We propose a compact, efficient, and end-to-end learning-based framework, namely AGD-Net. We first formulate the problem explicitly based on the classic gradient descent algorithm. AGD-Net can improve the reconstruction quality by more than 1.0 dB on average.
  arXiv  Detail & Related papers  (2021-08-12T05:54:09Z)
• Direct-PoseNet: Absolute Pose Regression with Photometric Consistency [16.60612964943876]
  We present a relocalization pipeline, which combines an absolute pose regression network with a novel view synthesis based direct matching module. Our network Direct-PoseNet achieves state-of-the-art performance among all other single-image APR methods on the 7-Scenes benchmark and the LLFF dataset.
  arXiv  Detail & Related papers  (2021-04-08T21:10:18Z)
• Domain-invariant Similarity Activation Map Contrastive Learning for Retrieval-based Long-term Visual Localization [30.203072945001136]
  In this work, a general architecture is first formulated probabilistically to extract domain invariant feature through multi-domain image translation. And then a novel gradient-weighted similarity activation mapping loss (Grad-SAM) is incorporated for finer localization with high accuracy. Extensive experiments have been conducted to validate the effectiveness of the proposed approach on the CMUSeasons dataset. Our performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision.
  arXiv  Detail & Related papers  (2020-09-16T14:43:22Z)
• Cross-Scale Internal Graph Neural Network for Image Super-Resolution [147.77050877373674]
  Non-local self-similarity in natural images has been well studied as an effective prior in image restoration. For single image super-resolution (SISR), most existing deep non-local methods only exploit similar patches within the same scale of the low-resolution (LR) input image. This is achieved using a novel cross-scale internal graph neural network (IGNN)
  arXiv  Detail & Related papers  (2020-06-30T10:48:40Z)
• The Power of Triply Complementary Priors for Image Compressive Sensing [89.14144796591685]
  We propose a joint low-rank deep (LRD) image model, which contains a pair of complementaryly trip priors. We then propose a novel hybrid plug-and-play framework based on the LRD model for image CS. To make the optimization tractable, a simple yet effective algorithm is proposed to solve the proposed H-based image CS problem.
  arXiv  Detail & Related papers  (2020-05-16T08:17:44Z)

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.