# Getting Started

The goal of this page is to briefly introduce you to our library and its capabilities. As soon as you have a successfully installed version of our library you can start experimenting with it.

For our CVPR paper we experimented with two datasets, summarized below:

The DTU dataset is an indoor dataset that contains a great variety of objects from different materials. It can be downloaded from here. On the contrary, the Aerial dataset is an outdoor dataset that contains images from urban environments captured from an aerial platform. It can be downloaded from here. In case you use any of these datasets please do not forget to cite the corresponding papers mentioned above!

## Train a simple Multi-View CNN model from scratch

As soon as you have downloaded one of these two datasets you can directly start training a simple Multi-View CNN model from scratch. For this particular example, we will train a Multi-View CNN model on the Aerial dataset for 40 epochs. We assume that the dataset was previously downloaded in the /tmp/aerial_dataset/. Using our raynet_pretrain console application you can easily start immediately training your model using one of the provided architectures.

Here follows the terminal output for the first 15 training epochs:


$CUDA_VISIBLE_DEVICES=0 raynet_pretrain /tmp/aerial_dataset/ /tmp/aerial_dataset /tmp/foo /path/to/config/restrepo_train_test_splits.json --lr 1e-3 --epochs 40 Using TensorFlow backend. {0: 'BH', 1: 'capitol', 2: 'downtown'} {0: 'BH', 1: 'capitol', 2: 'downtown'} Create '0SGLLKUKPUFE48VWNUQS' folder for current experiment Collecting test set... Building the octree for the current scene. Be patient... 500/500 [==============================] - 171s Cache 500 samples for training Building the octree for the current scene. Be patient... 499/500 [============================>.] - ETA: 0s Found device 0 with properties: name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate (GHz) 1.531 pciBusID 0000:02:00.0 Total memory: 11.91GiB Free memory: 11.75GiB 2018-08-21 13:21:10.090922: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0 2018-08-21 13:21:10.090928: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y 2018-08-21 13:21:10.090935: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: TITAN X (Pascal), pci bus id: 0000:02:00.0) Epoch 1/40 500/500 [==============================] - 67s - loss: 0.0749 - acc: 0.3473 - mae: 0.0429 - mde: 2.2181 - val_loss: 0.1275 - val_acc: 0.4060 - val_mae: 0.0393 - val_mde: 4.0400 Epoch 2/40 500/500 [==============================] - 66s - loss: 0.0533 - acc: 0.3991 - mae: 0.0396 - mde: 1.5912 - val_loss: 0.0515 - val_acc: 0.5420 - val_mae: 0.0292 - val_mde: 1.5800 Epoch 3/40 500/500 [==============================] - 68s - loss: 0.0474 - acc: 0.4094 - mae: 0.0386 - mde: 1.4214 - val_loss: 0.0482 - val_acc: 0.5780 - val_mae: 0.0279 - val_mde: 1.5080 Epoch 4/40 500/500 [==============================] - 68s - loss: 0.0446 - acc: 0.4129 - mae: 0.0379 - mde: 1.3374 - val_loss: 0.0405 - val_acc: 0.6480 - val_mae: 0.0236 - val_mde: 1.2420 Epoch 5/40 500/500 [==============================] - 69s - loss: 0.0449 - acc: 0.4104 - mae: 0.0380 - mde: 1.3582 - val_loss: 0.0865 - val_acc: 0.4700 - val_mae: 0.0339 - val_mde: 2.7700 Epoch 6/40 500/500 [==============================] - 68s - loss: 0.0429 - acc: 0.4310 - mae: 0.0370 - mde: 1.2906 - val_loss: 0.0365 - val_acc: 0.6200 - val_mae: 0.0245 - val_mde: 1.1460 Epoch 7/40 500/500 [==============================] - 68s - loss: 0.0396 - acc: 0.4381 - mae: 0.0363 - mde: 1.2038 - val_loss: 0.0467 - val_acc: 0.6060 - val_mae: 0.0258 - val_mde: 1.5280 Epoch 8/40 500/500 [==============================] - 68s - loss: 0.0402 - acc: 0.4160 - mae: 0.0373 - mde: 1.2304 - val_loss: 0.0453 - val_acc: 0.6040 - val_mae: 0.0249 - val_mde: 1.5080 Epoch 9/40 500/500 [==============================] - 68s - loss: 0.0424 - acc: 0.4230 - mae: 0.0371 - mde: 1.3170 - val_loss: 0.0441 - val_acc: 0.5940 - val_mae: 0.0260 - val_mde: 1.3320 Epoch 10/40 500/500 [==============================] - 68s - loss: 0.0416 - acc: 0.4304 - mae: 0.0365 - mde: 1.2781 - val_loss: 0.0477 - val_acc: 0.5760 - val_mae: 0.0266 - val_mde: 1.5620 Epoch 11/40 500/500 [==============================] - 68s - loss: 0.0374 - acc: 0.4555 - mae: 0.0352 - mde: 1.1437 - val_loss: 0.0649 - val_acc: 0.5760 - val_mae: 0.0267 - val_mde: 2.0180 Epoch 12/40 500/500 [==============================] - 68s - loss: 0.0395 - acc: 0.4561 - mae: 0.0351 - mde: 1.2291 - val_loss: 0.0444 - val_acc: 0.5840 - val_mae: 0.0258 - val_mde: 1.4680 Epoch 13/40 500/500 [==============================] - 68s - loss: 0.0413 - acc: 0.4189 - mae: 0.0368 - mde: 1.2853 - val_loss: 0.0437 - val_acc: 0.6180 - val_mae: 0.0242 - val_mde: 1.5040 Epoch 14/40 500/500 [==============================] - 68s - loss: 0.0385 - acc: 0.4556 - mae: 0.0348 - mde: 1.1827 - val_loss: 0.0381 - val_acc: 0.6080 - val_mae: 0.0248 - val_mde: 1.2140 Epoch 15/40 301/500 [=====================>........] - ETA: 54s - loss: 0.0372 - acc: 0.4373 - mae: 0.0363 - mde: 1.2138  As you can see during training, we report various metrics such as the loss and the accuracy both on the training and the validation set to be able to check that the network converges. Another thing worth mentioning has to do with the output of this console application, which is a folder in the output directory containing various statistics regarding training as well as all the trained models. As you can see in the terminal output above, for this run, our console application created a folder inside the output directory with name 0SGLLKUKPUFE48VWNUQS, where it will store all these data. ## Test our previously trained model As soon as we have finished with training our model, we can evaluate the learned model on a scene using the raynet_forward console application. For simplicity, for this example, we will test the trained model on the 20 first images of the DOWNTOWN scene. Below follows the corresponding terminal output: $ CUDA_VISIBLE_DEVICES=0 raynet_forward /tmp/aerial_dataset/ /tmp/foo/0SGLLKUKPUFE48VWNUQS/depth_maps/ --weight_file /tmp/foo/0SGLLKUKPUFE48VWNUQS/weights/weights.39.hdf5 --scene_idx 2 --network_architecture simple_cnn--forward_pass_factory multi_view_cnn --dataset_type restrepo --start_end 0,20
Using TensorFlow backend.
StreamExecutor works with that.
2018-08-21 14:31:06.013088: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: TITAN X (Pascal)
major: 6 minor: 1 memoryClockRate (GHz) 1.531
pciBusID 0000:02:00.0
Total memory: 11.91GiB
Free memory: 11.60GiB
2018-08-21 14:31:06.013104: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0
2018-08-21 14:31:06.013110: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0:   Y
2018-08-21 14:31:06.013116: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: TITAN X (Pascal), pci bus id: 0000:02:00.0)
{0: 'BH', 1: 'capitol', 2: 'downtown'}
Features computation -  2.194076
Per-pixel depth estimation -  0.679471
Features computation -  0.659598
Per-pixel depth estimation -  0.065472
Features computation -  0.636852
Per-pixel depth estimation -  0.064682
Features computation -  0.630682
Per-pixel depth estimation -  0.065272
Features computation -  0.634153
Per-pixel depth estimation -  0.066705
Features computation -  0.647827
Per-pixel depth estimation -  0.067191
Features computation -  0.638852
Per-pixel depth estimation -  0.065388
Features computation -  0.631912
Per-pixel depth estimation -  0.064861
Features computation -  0.632437
Per-pixel depth estimation -  0.065154
Features computation -  0.64483
Per-pixel depth estimation -  0.064949
Features computation -  0.631402
Per-pixel depth estimation -  0.066548
Features computation -  0.700359
Per-pixel depth estimation -  0.066643
Features computation -  0.633139
Per-pixel depth estimation -  0.064806
Features computation -  0.633135
Per-pixel depth estimation -  0.064748
Features computation -  0.673216
Per-pixel depth estimation -  0.065133
Features computation -  0.685232
Per-pixel depth estimation -  0.064738
Features computation -  0.632668
Per-pixel depth estimation -  0.065855
Features computation -  0.628777
Per-pixel depth estimation -  0.065074
Features computation -  0.637438
Per-pixel depth estimation -  0.066568
Features computation -  0.6403
Per-pixel depth estimation -  0.065694


This console application creates a set of depth maps, one for every input view based on the learned model. These depth maps are stored as numpy arrays in the /tmp/foo/0SGLLKUKPUFE48VWNUQS/depth_maps/ folder. Below we visualize some of the predicted depth maps just by using the Multi-View CNN. As you can easily notice the depth predictions are quite noisy due to the small receptive field of the architecture and the small during of training (only 40 epochs)

Depth predictions after training a Multi-View CNN with receptive field $$11 \times 11$$ for 40 epochs.