Metric3D v2: 一个多功能单眼几何基础模型,用于零拍摄的度量深度和表面正常估计
IEEE transactions on pattern analysis and machine intelligence
|August 16, 2024
概括
Metric3D v2,一个几何基础模型,从单个图像中实现零拍摄的度量深度和表面正常估计. 这通过解决尺度模糊性和提高正常预测准确性来推进3D恢复和单图像计量学.
科学领域:
- 计算机视觉 计算机视觉
- 几何深度学习 几何深度学习
- 三维重建的3D重建
背景情况:
- 单眼深度估计方法往往对现实世界度量尺度的概括很差.
- 由于有限的标记数据,表面正常估计在零射击性能方面面临挑战.
- 从单个图像中精确的3D恢复需要精确的尺度深度和表面正常信息.
研究的目的:
- 介绍Metric3D v2,一个用于零射击度深度和表面正常估计的几何基础模型.
- 解决现有方法在恢复度量尺度和实现强大的零射击概括方面的局限性.
- 为了实现精确的单图像计量,并改进单眼同步定位和映射 (SLAM).
主要方法:
- 开发了一种正规的相机空间转换模块,以解决深度估计中的度量模糊性.
- 引入了一个联合深度-正常优化模块,利用度量深度数据来增强正常估计.
- 训练了该模型的超过1600万张图像从不同的相机模型与各种注释.
主要成果:
- Metric3D v2实现了对新摄像头设置进行最先进的零拍摄概括.
- 该模型在多个零射击和标准基准测试中排名第一,用于度量深度和表面正常预测.
- 从互联网图像中证明了精确的度量3D结构恢复,并在单眼SLAM中减少了尺度漂移.
结论:
- Metric3D v2 作为一个多功能几何基础模型,用于单图像3D恢复.
- 拟议的方法在零射击设置中显著提高了度量深度和表面正常估计的准确性.
- 该模型为单图像计量学和密集3D绘图的先进应用铺平了道路.
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