SCR: Smooth Contour Regression with Geometric Priors

• URL: http://arxiv.org/abs/2202.03784v1
• Date: Tue, 8 Feb 2022 11:07:51 GMT
• Title: SCR: Smooth Contour Regression with Geometric Priors
• Authors: Gaetan Bahl, Lionel Daniel, Florent Lafarge
• Abstract summary: SCR is a method that captures resolution-free object contours as complex periodic functions. We benchmark SCR on the popular COCO 2017 instance segmentation dataset, and show its competitiveness against existing algorithms. We also design a compact version of our network, which we benchmark on embedded hardware with a wide range of power targets.
• Score: 10.141085397402314
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While object detection methods traditionally make use of pixel-level masks or bounding boxes, alternative representations such as polygons or active contours have recently emerged. Among them, methods based on the regression of Fourier or Chebyshev coefficients have shown high potential on freeform objects. By defining object shapes as polar functions, they are however limited to star-shaped domains. We address this issue with SCR: a method that captures resolution-free object contours as complex periodic functions. The method offers a good compromise between accuracy and compactness thanks to the design of efficient geometric shape priors. We benchmark SCR on the popular COCO 2017 instance segmentation dataset, and show its competitiveness against existing algorithms in the field. In addition, we design a compact version of our network, which we benchmark on embedded hardware with a wide range of power targets, achieving up to real-time performance.

Related papers

• Segmenting objects with Bayesian fusion of active contour models and convnet priors [0.729597981661727]
  We propose a novel instance segmentation method geared towards Natural Resource Monitoring (NRM) imagery. We formulate the problem as Bayesian maximum a posteriori inference which, in learning the individual object contours, incorporates shape, location, and position priors. In experiments, we tackle the challenging, real-world problem of segmenting individual dead tree crowns and precise contours.
  arXiv  Detail & Related papers  (2024-10-09T20:36:43Z)
• Geometry-aware Feature Matching for Large-Scale Structure from Motion [10.645087195983201]
  We introduce geometry cues in addition to color cues to fill gaps when there is less overlap in large-scale scenarios. Our approach ensures that the denser correspondences from detector-free methods are geometrically consistent and more accurate. It outperforms state-of-the-art feature matching methods on benchmark datasets.
  arXiv  Detail & Related papers  (2024-09-03T21:41:35Z)
• 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)
• KP-RED: Exploiting Semantic Keypoints for Joint 3D Shape Retrieval and Deformation [87.23575166061413]
  KP-RED is a unified KeyPoint-driven REtrieval and Deformation framework. It takes object scans as input and jointly retrieves and deforms the most geometrically similar CAD models.
  arXiv  Detail & Related papers  (2024-03-15T08:44:56Z)
• Leveraging Positional Encoding for Robust Multi-Reference-Based Object 6D Pose Estimation [21.900422840817726]
  Accurately estimating the pose of an object is a crucial task in computer vision and robotics. In this paper, we analyze these limitations and propose new strategies to overcome them. Our experiments on Linemod, Linemod-Occlusion, and YCB-Video datasets demonstrate that our approach outperforms existing methods.
  arXiv  Detail & Related papers  (2024-01-29T16:42:15Z)
• CRA-PCN: Point Cloud Completion with Intra- and Inter-level Cross-Resolution Transformers [29.417270066061864]
  We present Cross-Resolution Transformer that efficiently performs cross-resolution aggregation with local attention mechanisms. We integrate two forms of Cross-Resolution Transformers into one up-sampling block for point generation, and following the coarse-to-fine manner, we construct CRA-PCN to incrementally predict complete shapes.
  arXiv  Detail & Related papers  (2024-01-03T05:57:39Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• Geometric Disentanglement by Random Convex Polytopes [3.6852491526879683]
  We propose a new geometric method for measuring the quality of representations obtained from deep learning. Our approach, called Random Polytope Descriptor, provides an efficient description of data points based on the construction of random convex polytopes.
  arXiv  Detail & Related papers  (2020-09-29T13:16:26Z)
• Geometry Constrained Weakly Supervised Object Localization [55.17224813345206]
  We propose a geometry constrained network, termed GC-Net, for weakly supervised object localization. The detector predicts the object location defined by a set of coefficients describing a geometric shape. The generator takes the resulting masked images as input and performs two complementary classification tasks for the object and background. In contrast to previous approaches, GC-Net is trained end-to-end and predict object location without any post-processing.
  arXiv  Detail & Related papers  (2020-07-19T17:33:42Z)
• Dense Non-Rigid Structure from Motion: A Manifold Viewpoint [162.88686222340962]
  Non-Rigid Structure-from-Motion (NRSfM) problem aims to recover 3D geometry of a deforming object from its 2D feature correspondences across multiple frames. We show that our approach significantly improves accuracy, scalability, and robustness against noise.
  arXiv  Detail & Related papers  (2020-06-15T09:15:54Z)
• Neural Subdivision [58.97214948753937]
  This paper introduces Neural Subdivision, a novel framework for data-driven coarseto-fine geometry modeling. We optimize for the same set of network weights across all local mesh patches, thus providing an architecture that is not constrained to a specific input mesh, fixed genus, or category. We demonstrate that even when trained on a single high-resolution mesh our method generates reasonable subdivisions for novel shapes.
  arXiv  Detail & Related papers  (2020-05-04T20:03:21Z)

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.