NeRD: Neural 3D Reflection Symmetry Detector (2105.03211v1)

Abstract: Recent advances have shown that symmetry, a structural prior that most objects exhibit, can support a variety of single-view 3D understanding tasks. However, detecting 3D symmetry from an image remains a challenging task. Previous works either assume that the symmetry is given or detect the symmetry with a heuristic-based method. In this paper, we present NeRD, a Neural 3D Reflection Symmetry Detector, which combines the strength of learning-based recognition and geometry-based reconstruction to accurately recover the normal direction of objects' mirror planes. Specifically, we first enumerate the symmetry planes with a coarse-to-fine strategy and then find the best ones by building 3D cost volumes to examine the intra-image pixel correspondence from the symmetry. Our experiments show that the symmetry planes detected with our method are significantly more accurate than the planes from direct CNN regression on both synthetic and real-world datasets. We also demonstrate that the detected symmetry can be used to improve the performance of downstream tasks such as pose estimation and depth map regression. The code of this paper has been made public at https://github.com/zhou13/nerd.

• The paper introduces a novel neural network architecture to detect reflection symmetry in 3D models, enhancing accuracy over traditional methods.
• It leverages deep learning to precisely analyze geometric features, enabling efficient identification of symmetry in complex structures.
• Experimental evaluations demonstrate the method’s robustness and its potential applications in computer vision and robotics.

Overview of \LaTeX\ Author Guidelines for CVPR Proceedings

The paper "\LaTeX\ Author Guidelines for CVPR Proceedings" provides comprehensive instructions for authors wishing to submit manuscripts to the Conference on Computer Vision and Pattern Recognition (CVPR). This document outlines the formal requirements and specific formatting protocols mandated by the IEEE Computer Society Press for paper submission, demonstrating a meticulous approach to standardizing academic submissions in the field of computer vision and related disciplines.

Structural and Formatting Norms

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Blind Review Process

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Page Layout and Submission Specifics

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Speculation on Future Developments

Looking towards future developments in AI and computer vision, guidelines such as these may evolve to encompass new types of data presentation made possible by advances in digital publishing. There could be an increased focus on interactive components, dynamic visualizations, and perhaps integration with online repositories or collaborative platforms. However, the core tenets of clarity, professionalism, and adherence to standardization are likely to remain integral to the submission and review processes.

In conclusion, the document "\LaTeX\ Author Guidelines for CVPR Proceedings" is a quintessential resource for authors in the field, meticulously outlining the necessary steps and considerations for writing and submitting scientific papers. Its role in upholding the standards of academic communication within the domain of computer vision cannot be overstated.