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Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

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.

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Updated: Apr 29, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

学习虚拟视图选择3D场景语义细分学习3D场景语义细分

Tai-Jiang Mu, Ming-Yuan Shen, Yu-Kun Lai

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

|July 10, 2024

概括

此摘要是机器生成的。

这项研究通过生成信息化的虚拟2D视图,为3D场景的理解引入了一个新的框架. 这种方法通过克服现实世界捕获图像的局限性来提高3D语义细分的准确性.

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A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

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Published on: November 30, 2022

相关实验视频

Last Updated: Apr 29, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
机器学习机器学习

背景情况:

联合2D-3D学习对于3D视觉任务至关重要,例如语义细分,利用互补数据.
目前仅使用真实2D图像的方法存在冗余性,遮蔽性和有限的视野,阻碍了性能.
有效的3D场景理解需要克服标准2D图像输入的局限性.

研究的目的:

通过选择信息化的虚拟2D视图,提出一个共同理解2D-3D场景的总框架.
通过将生成的虚拟视图与3D几何数据集成来改进3D语义细分.
通过一种新的视图选择策略,增强3D视觉任务的深度神经模型.

主要方法:

基于从3D场景语义细分结果获得的信息得分图生成虚拟的2D视图.
将信息得分地图学习形式化为深度强化学习过程,为准确的预测提供奖励.
在6D空间 (坐标和正常) 采用高效的贪虚拟视图覆盖策略,以获得最佳的表面覆盖.

主要成果:

验证了ScanNet v2和S3DIS数据集的框架,证明了与基线模型相比的一致的收益.
实现了新的最先进的准确性,用于联合2D和3D场景语义分割.
提出的方法有效地提高了联合2D-3D和纯3D输入深度神经模型的性能.

结论:

拟议的虚拟视图选择框架显著提高了3D场景的理解.
这种方法有效地解决了现实世界2D图像数据在3D视觉任务中的局限性.
该方法为改善3D语义细分中的深度学习模型提供了通用和有效的解决方案.