Deep vanishing point detection: Geometric priors make dataset variations vanish

• URL: http://arxiv.org/abs/2203.08586v1
• Date: Wed, 16 Mar 2022 12:34:27 GMT
• Title: Deep vanishing point detection: Geometric priors make dataset variations vanish
• Authors: Yancong Lin, Ruben Wiersma, Silvia L. Pintea, Klaus Hildebrandt, Elmar Eisemann, and Jan C. van Gemert
• Abstract summary: Deep learning has improved vanishing point detection in images. Yet, deep networks require expensive annotated datasets trained on costly hardware. Here, we address these issues by injecting deep vanishing point detection networks with prior knowledge.
• Score: 24.348651041697114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has improved vanishing point detection in images. Yet, deep networks require expensive annotated datasets trained on costly hardware and do not generalize to even slightly different domains, and minor problem variants. Here, we address these issues by injecting deep vanishing point detection networks with prior knowledge. This prior knowledge no longer needs to be learned from data, saving valuable annotation efforts and compute, unlocking realistic few-sample scenarios, and reducing the impact of domain changes. Moreover, the interpretability of the priors allows to adapt deep networks to minor problem variations such as switching between Manhattan and non-Manhattan worlds. We seamlessly incorporate two geometric priors: (i) Hough Transform -- mapping image pixels to straight lines, and (ii) Gaussian sphere -- mapping lines to great circles whose intersections denote vanishing points. Experimentally, we ablate our choices and show comparable accuracy to existing models in the large-data setting. We validate our model's improved data efficiency, robustness to domain changes, adaptability to non-Manhattan settings.

Related papers

• Revisiting Cross-Domain Problem for LiDAR-based 3D Object Detection [5.149095033945412]
  We deeply analyze the cross-domain performance of the state-of-the-art models. We observe that most models will overfit the training domains and it is challenging to adapt them to other domains directly. We propose additional evaluation metrics -- the side-view and front-view AP -- to better analyze the core issues of the methods' heavy drops in accuracy levels.
  arXiv  Detail & Related papers  (2024-08-22T19:52:44Z)
• Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers [59.0181939916084]
  Traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries. We propose a novel Priors Distillation (RPD) method to extract priors from the well-trained transformers on massive images. Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification.
  arXiv  Detail & Related papers  (2024-07-26T06:29:09Z)
• CMG-Net: Robust Normal Estimation for Point Clouds via Chamfer Normal Distance and Multi-scale Geometry [23.86650228464599]
  This work presents an accurate and robust method for estimating normals from point clouds. We first propose a new metric termed Chamfer Normal Distance to address this issue. We devise an innovative architecture that encompasses Multi-scale Local Feature Aggregation and Hierarchical Geometric Information Fusion.
  arXiv  Detail & Related papers  (2023-12-14T17:23:16Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• Capsules as viewpoint learners for human pose estimation [4.246061945756033]
  We show how most neural networks are not able to generalize well when the camera is subject to significant viewpoint changes. We propose a novel end-to-end viewpoint-equivariant capsule autoencoder that employs a fast Variational Bayes routing and matrix capsules. We achieve state-of-the-art results for multiple tasks and datasets while retaining other desirable properties.
  arXiv  Detail & Related papers  (2023-02-13T09:01:46Z)
• Deep Surface Reconstruction from Point Clouds with Visibility Information [66.05024551590812]
  We present two simple ways to augment raw point clouds with visibility information, so it can directly be leveraged by surface reconstruction networks with minimal adaptation. Our proposed modifications consistently improve the accuracy of generated surfaces as well as the generalization ability of the networks to unseen shape domains.
  arXiv  Detail & Related papers  (2022-02-03T19:33:47Z)
• Weakly Supervised Change Detection Using Guided Anisotropic Difusion [97.43170678509478]
  We propose original ideas that help us to leverage such datasets in the context of change detection. First, we propose the guided anisotropic diffusion (GAD) algorithm, which improves semantic segmentation results. We then show its potential in two weakly-supervised learning strategies tailored for change detection.
  arXiv  Detail & Related papers  (2021-12-31T10:03:47Z)
• Pretrained equivariant features improve unsupervised landmark discovery [69.02115180674885]
  We formulate a two-step unsupervised approach that overcomes this challenge by first learning powerful pixel-based features. Our method produces state-of-the-art results in several challenging landmark detection datasets.
  arXiv  Detail & Related papers  (2021-04-07T05:42:11Z)
• Improving Deep Stereo Network Generalization with Geometric Priors [93.09496073476275]
  Large datasets of diverse real-world scenes with dense ground truth are difficult to obtain. Many algorithms rely on small real-world datasets of similar scenes or synthetic datasets. We propose to incorporate prior knowledge of scene geometry into an end-to-end stereo network to help networks generalize better.
  arXiv  Detail & Related papers  (2020-08-25T15:24:02Z)

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.