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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: Feb 27, 2026

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

腿部:视觉定位增强了3D高斯斯点.

Daewoon Kim¹, I-Gil Kim¹

¹Tech. Innovation Group, KT Corporation, 151, Taebong-ro, Seocho-gu, Seoul 06763, Republic of Korea.

Journal of imaging

|February 26, 2026

概括

此摘要是机器生成的。

腿部通过使用3D高斯分片生成有用的合成摄像头视图来增强视觉本地化. 这提高了绘图和导航的准确性和稳定性,特别是在稀疏数据的情况下.

关键词:

3D高斯式喷涂 (3DGS) 是一种三维的方法.小说视图合成 (NVS)结构-从-运动 (SfM)摄像机姿势估计的估计.合成视图增强器增强器视觉定位视觉定位

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科学领域:

计算机视觉计算机视觉
机器人技术机器人技术机器人技术
3D重建的3D重建

背景情况:

精确的六度自由度 (6-DoF) 视觉定位对于绘图和导航至关重要.
目前使用新视图合成 (NVS) 进行数据集增强的方法通常会产生冗余或无信息的虚拟摄像头视图.
这限制了将稀疏的现实世界捕捉与密集的场景几何结构相结合的有效性.

研究的目的:

介绍LEGS (通过3D高斯分片增强的视觉定位),这是一个用于轨迹不可知合成视图增强的新框架.
提高视觉定位培训数据集的合成视图的质量和信息性.
为了提高6-DoF视觉定位的准确性和稳定性.

主要方法:

腿部集成一个粗的3D网格与结构-从-运动 (SfM) 摄像机图表提出摄像机姿势.
一个以可见性为导向,以覆盖率为导向的选择策略选择了最有信息的姿势.
使用3D高斯分片 (3DGS) 进行高效的,场景特定的合成视图生成.

主要成果:

在标准基准和内部数据集中,LEGS 始终提高了 6-DoF 姿势的准确性和稳定性.
该框架表现出有效性,特别是在具有挑战性的场景中,相机采样稀疏,视角共定位.
高通量,场景特定的合成在实际的计算限制内实现.

结论:

LEGS在合成视图增强中为视觉定位提供了显著的进步.
拟议的方法有效地解决了基于NVS的方法中天真视图采样的局限性.
LEGS为增强视觉定位系统提供了一种实用且计算效率高的解决方案.