车辆2D/3D--Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis
2017-07-27 09:26
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Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image
CVPR2017
https://arxiv.org/abs/1703.07570
自动驾驶 很快就可以达到实用的水平了。
本文的功能是：给一张灰度图像，使用 多任务CNN网络 Deep MANTA 可以给出6个信息： region proposal, detection, 2D box regression, part localization, part visibility and 3D template prediction，通过定义 Many-task loss functions 实现
先上图来个感性认识：
Deep MANTA 整个网络流程图如下所示：
Conv layers with the same color share the same weights
怎么从2D 信息推理出 3D 信息了？
首先我们利用了2个3D 的数据库 3D shape and template datasets
2D/3D vehicle model
数据标记问题怎么解决
Semi-automatic annotation process
Experiments
http://www.cvlibs.net/datasets/kitti/eval_object_detail.php?&result=6759889c0a252c63765d5e2e69cb8b1433cadb0a
Running time: 0.7 s
Environment: GPU @ 2.5 Ghz (Python + C/C++)
CVPR2017
https://arxiv.org/abs/1703.07570
自动驾驶 很快就可以达到实用的水平了。
本文的功能是：给一张灰度图像，使用 多任务CNN网络 Deep MANTA 可以给出6个信息： region proposal, detection, 2D box regression, part localization, part visibility and 3D template prediction，通过定义 Many-task loss functions 实现
先上图来个感性认识：
Deep MANTA 整个网络流程图如下所示：
Conv layers with the same color share the same weights
怎么从2D 信息推理出 3D 信息了？
首先我们利用了2个3D 的数据库 3D shape and template datasets
2D/3D vehicle model
数据标记问题怎么解决
Semi-automatic annotation process
Experiments
http://www.cvlibs.net/datasets/kitti/eval_object_detail.php?&result=6759889c0a252c63765d5e2e69cb8b1433cadb0a
Running time: 0.7 s
Environment: GPU @ 2.5 Ghz (Python + C/C++)
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