通過3DOP專案的主頁，我們可以找到通往多倫多大學的專案原始碼的連結。不知是處於什麼目的，該程式碼是以壓縮包而不是github的方式給出的。

通過下載之後，我們可以看到指導說明為：

3DOP_code_cuDNNv3 and v5

由於cuDNN 3版本和cuDNN5的版本上有一定實現差異，因此該程式碼相容了上述兩個版本。下面僅針對cuDNN v5 來進行程式碼分析。

• ## 3D Object Proposals (3DOP)
  This is an implementation of the algorithms described in

3D Object Proposals for Accurate Object Class Detection.
Xiaozhi Chen*, Kaustav Kunku*, Yukun Zhu, Andrew Berneshawi, Huimin Ma, Sanja Fidler and Raquel Urtasun. NIPS, 2015.

Precomputed Results

We provide following precomputed models and results on our project page: http://www.cs.toronto.edu/objprop3d/
- Train-val split of KITTI training set
- Disparity computed by SPS-stereo for KITTI training and testing sets
- Road planes for KITTI training and testing sets
- 2D/3D proposals for KITTI training set
- CNN models trained with 3DOP on KITTI training set
- 2D/3D detection results using 3DOP on KITTI validation set

Compute Proposals

Please refer to ./3DOP/demo_3dop.m for training/testing/evaluation of the proposals.

Object Detection Networks

Detection code is under ./frcn-kitti/
This implementation is built on Fast R-CNN (https://github.com/rbgirshick/fast-rcnn).

1. Build the Cython modules

   • cd ./frcn-kitti/lib
   • make

2. Prepare data

   • KITTI images are supposed to be placed unber ./frcn-kitti/data/kitti/object/
   • 3D object proposals are supposed to be placed under ./frcn-kitti/data/proposals/

3. Download pre-trained ImageNet models

   • cd ./frcn-kitti
   • ./data/scripts/fetch_imagenet_models.sh

4. Compile KITTI evaluation code

   • cd ./frcn-kitti/kitti/eval/cpp
   • g++ evaluate_object.cpp -o evaluate_object

5. Training & testing

Please refer to ./frcn-kitti/demo.sh for training/testing of the detection networks.

ImageSets

ImageSets就是將KITTI中的7517張圖片，隨機均勻地劃分成兩部分。一部分用作test，另外一部分用作validate。兩部分的圖片數量大致在3750張左右。

其他部分的內容將在下載完成之後進行相關的測試。

參考文獻