[For Pro Users] If you have your own dataset with point clouds and annotated 3D bounding boxes, you can create a new dataset class and train VoteNet on your own data. To ease the proces, some tips are provided below.
Firstly, you need to store point clouds in the upright coordinate system (Z is up, Y is forward, X is right-ward) and 3D bounding boxes as its center (x,y,z), size (l,w,h) and heading angle (along the up-axis; rotation radius from +X towards -Y; +X is 0 and -Y is pi/4). You can refer to sunrgbd/sunrgbd_data.py as to how to compute the groundtruth votes (translational vectors from object points to 3D bounding box centers). If your dataset has instance segmentation annotation, you can also compute groundtruth votes on the fly in the dataset class -- refer to scannet/batch_load_scannet_data.py and scannet/scannet_detection_dataset.py for more details.
Secondly, you need to create a new dataset class as well as to specify some config information about the dataset. For config information, you can refer to sunrgbd/model_util_config.py as an example and modify the num_classes, type2class, num_size_clusters, mean_size_arr etc. The mean_size_arr is computed by going through all 3D bounding boxes in the train set and cluster them (either by geometric size or semantic class) into several clusters and then compute the median box size in each cluster (an example porcess is here). In both SUN RGB-D and ScanNet, we only consider one tempalte box size for each semantic class, but you can have multiple size templates for each class too (in which case you also need to modify the size2class function in the config). For detection dataset class, you can refer to sunrgbd/sunrgbd_detection_dataset.py and modify based on it. The major thing to modify is the dataset paths (in __init__ function) and data loading methods (at the beginning of the __getitem__ function), which depend on where and how you store the data.
Lastly, after you make sure the dataset class returns the correct input point clouds and ground truth labels, you need to add the new dataset to the train.py file and eval.py file by augmenting the options of FLAGS.dataset argument (adding another elif to the dataset set up section). Then by selecting your new dataset in train.py, you should be able to train a VoteNet on your own data!
Note that the VoteNet was originally used on SUN RGB-D and ScanNet which only have either 1D or 0D rotations in their annotated bounding boxes. It is possible to extend the VoteNet to predict 3D rotations though. One simple way is to supervise the network to predict three Euler angles. To support it you will need to prepare ground truth labels and then change the prediction of the 1D heading_angle to prediction of three Euler angles in the network output; and modify the 3D bounding box parameterization and transformations accordingly.
Feel free to post an issue if you meet any difficulty during the process!