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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

751
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
751

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通过对染的2D图像进行主动学习,高效的3D场景语义细分.

Mengqi Rong, Hainan Cui, Shuhan Shen

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |June 20, 2023
    PubMed
    概括

    本研究引入了使用染的2D图像和主动学习进行3D场景语义细分的新框架. 该方法有效地分割大规模的3D场景,最小的2D图像注释.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 3D场景理解 3D场景理解

    背景情况:

    • 大规模3D场景的语义细分是计算密集的,需要大量的注释.
    • 现有的方法在效率上扎,需要详细的3D数据标签.

    研究的目的:

    • 为3D场景语义细分开发一个标签效率高的框架.
    • 为了利用二维图像分析和主动学习来准确地理解3D场景.

    主要方法:

    • 一个结合主动学习和2D-3D语义融合的新框架.
    • 从3D场景中染视角的2D图像.
    • 预先训练的语义细分网络的代微调.
    • 将密集的2D预测投射到3D模型中,用于融合和改进.

    主要成果:

    • 该框架实现了大规模3D场景的高效语义细分.
    • 在三个大型室内和室外数据集上证明了有效性.
    • 在标签效率高的3D细分方面超越了其他最先进的方法.

    结论:

    • 拟议的染-细分-融合代过程有效地产生了具有挑战性的训练样本.

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  • 这种方法显著减少了复杂3D注释的需求.
  • 可实现标签效率高,准确的3D场景语义细分.