车辆三维重建MATLAB源码，对模型训练后可以对车辆图片进行三维重建。.zip

##Category-Specific Object Reconstruction from a Single Image (CVPR 2015) #### [Abhishek Kar\*](http://cs.berkeley.edu/~akar), [Shubham Tulsiani\*](http://cs.berkeley.edu/~shubhtuls), [Joao Carreira](http://cs.berkeley.edu/~carreira), [Jitendra Malik](http://cs.berkeley.edu/~malik) ### Datasets and Paths: You will need [**PASCAL VOC 2012**](http://host.robots.ox.ac.uk:8080/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar) and [**PASCAL 3D+**](http://cvgl.stanford.edu/projects/pascal3d.html) to run the code. Download them and change the paths in `startup.m` to reflect your paths. ### Compilation: Run `compile.m`. Compile the vlfeat library under `external/` ### Data: ``` $ sh setup_data.sh ``` The above script will download data for the project, PASCAL VOC 2012 and PASCAL 3D+ and put it under `data/`. It will also download and extract `vlfeat` in the `external/`. All the required data can be downloaded from [here](http://cs.berkeley.edu/~akar/categoryShapes/data.tar.gz). Unzip in `BASE_DIR/data` where `BASE_DIR` is the root directory for the codebase. The code needs data formatted as in `data/pascalData` with keypoints and segmentations (either as polygons or binary masks). It also needs keypoint names as in data/partNames and the train/val split in PASCAL as in `data/pascalTrainValIds` and metadata about keypoints as in `data/voc_kp_metadata` to align to a canonical frame (here the Pascal 3D frame) and ensure a right handed co-ordinate system. ### Train Models: ``` mainTrain('car','debug',<parameter options>) ``` This will train basis shape models for a particular class (here 'car') with the experiment id `'debug'` that you can use later to visualize/test/evaluate. Look at `get_params.m` for description of parameters. ### Test Models: ``` jobID = mainTest('car','debug','withKps',<parameter options>) ``` This will test the model you trained above (with train id `'debug'`) on the validation set. The test id (here `'withKps'`) is to enable testing with the same trained model with different test settings (e.g. with or without keypoints, with different parameters, with or without optimizing scale, translation, rotation etc). ### Visualizing Results: ``` visInferredShapes('car', jobID) ``` Visualize the results for the models above. `jobID` is returned by `mainTest` after execution. It is usually `'Test<trainId><testId>'`. Use this id to perform all operations on the test data (evaluation/visualization etc). ``` visNRSFMmodel('car', jobID) ``` Visualize the result of NRSFM. Shows the predicted 3D keypoints and their convex hull. ``` visDeformations('car', trainId) ``` Visualize the learnt deformation bases. Note: use `trainId` instead of `jobID` here. ### Evaluation: ``` mainEval('car',jobID); ``` This will run the mesh and depth map evaluation on the cached meshes and depth maps. ### Cached files: All the files are cached under in `'cachedir'` which is usually `./cache`. The cached files are named as follows: - `shapeModelNRSFM.mat` - Parameters for the trained NRSFM model. Also contains `test_model` which contains the estimated parameters on the test set - `shapeModelOpt<id>.mat` - Parameters for the trained basis shape models. - `statesDir<id>` - Directory containing the projection parameters from NRSFM and other info per instance. - `inferredShapes<id>` - Directory containing fitted basis shapes to instances, projection parameters after optimization etc. - `meshes<id>` - Directory containing meshes fitted to each instance. Each file contains `'faces'` and `'vertices'`. Can be viewed using the showMesh function - `depthMap<id>` - Directory containing meshes rendered into depth maps per instance. - `sirfs<id>` - Directory containing the outputs from SIRFS. Contains albedo, depth, normals, shading and illumination maps.