3D Bounding Box Estimation Using Deep Learning and Geometry论文理解
文章目录 解决什么问题 方法 方法概述 问题分析 3D bounding box estimation 训练 角度loss 维度loss 总loss 实验 总结 解决什么问题 从一个单视野恢复3D bounding box，在没有额外的3D形状模型、或有着复杂的预处理通道的采样策略下，估计稳定且精准3D目标 方法 方法概述 先直接回归方向和目标维度，用的是CNN方法。给定估计的方向和尺寸，以及3D
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相关文章
- 1. 3D Bounding Box Estimation Using Deep Learning and Geometry
- 2. 论文翻译：2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning
- 3. 姿态估计“2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning”
- 4. Geometry Meets Deep Learning(专栏)
- 5. Deep Learning Models and Code for Pose Estimation
- 6. 《Distance-IoU Loss：Faster and Better Learning for Bounding Box Regression》论文笔记
- 7. 读书笔记25：2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning（CVPR2018）
- 8. 【论文】Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
- 9. Joint Transmission Map Estimation and Dehazing using Deep Networks
- 10. 【论文笔记——DIM】Learning Deep Representations By Mutual Information Estimation and Maximization