# ALReg: Registration of 3D Point Clouds Using Active Learning
## 4 Experiments  
To evaluate our method, we have done experiments using ModelNet40 (for DCP and DeepBBS) and 7 Scenes (for FMR) datasets. We test the ALReg method against random selection (RAND) and also report the original results (FULL) obtained using the official source code. We performed all the experiments on a computer with an NVIDIA RTX A5000 GPU and Intel(R) Core(TM) i7-7700K CPU @ 4.20 GHz. We used the default hyperparameters of the mentioned methods. In FMR we used Chamfer loss and geometric loss. In DCP we used an MSE based loss as  
\[L_{DCP}=||R_{gt}^{T}R_{pred}-I||^{2}+||t_{gt}-t_{pred}||^{2}+\lambda||\theta||^ {2} \tag{11}\]  
where \(R_{gt}\) and \(t_{gt}\) refer to ground truth rotation matrix and translation vector and \(R_{gt}\) and \(t_{gt}\) refer to their predictions. The last part is a network regularization. For DeepBBS, the used loss function is  
\[L_{DeepBBS}=||R_{gt}^{T}R_{pred}-I||^{2}+||t_{gt}-t_{pred}||^{2}+\beta^{n}\frac {1}{N}\sum_{i=1}^{N}\gamma_{gt,i}||q_{i}-(R_{gt}p_{i}+t_{g}t)||^{2} \tag{12}\]  
where \(\gamma_{gt,i}\) is an indicator showing whether \(p_{i}\) and \(q_{i}\) are matching points in the source and target point clouds, \(\beta\) is a multiplier decreasing according to epoch number \(n\). Here \(\beta\) is selected as 0.95. For all experiments, the Adam [83] optimizer is used with a learning rate of 0.001 and the batch size is selected as 32. The only difference between our setup and the baselines is placing dropouts with \(p=0.25\) after each layer of the encoders. After the last active selection phase, the dropouts are removed from the networks for better convergence. Active selection is done after epochs [20; 50; 70] for DCP/DeepBBS and after epochs [40; 60; 80] for FMR. For DCP (the second version containing a transformer) and FMR, the official weights are directly used. However, DeepBBS which originally uses 786 sampled points in both train and test time, is retrained using 1024 points to ensure a fair comparison. For both networks, using the common procedure, a rotation of \([0,\ \frac{\pi}{4}]\) and translation of \([-0.5,\ 0.5]\) is used. For FMR, a rotation of \([0,\ \frac{\pi}{3}]\) and and translation of \([-0.5,\ 0.5]\) is used following the original setup. 7 Scenes is an indoor 3D scan dataset that contains 296 objects having an average of points and it has 57 test point cloud objects. Since 7 Scenes is a dense dataset, in full FMR, randomly sampled 10,000 points are used during training and testing. In our activelearning setup, we used VOSS [84] algorithm to divide each object into \(\sim\)150 superpoints. For each superpoint, 20 sampled points from the superpoint are fed to the network. Initially, 3 superpoints (60 points) of each object are used for training. After the last active selection phase, only 4.16% of the training set is selected. ModelNet40 is a CAD dataset of daily life objects containing 9840 training samples and 2468 test samples from 40 different categories. In the aforementioned registration methods, it is a rule of thumb to use 1024 points from CAD model for each object. Using random sampling, \(\sim\)100 superpoints are created for each object as given in Figure 3.  
After training the networks with the mentioned datasets, we also evaluated their accuracy on 3 DMatch to show their consistency on unseen data. 3 DMatch is a real-life scan dataset containing a total of 520 samples in its test set. Since we evaluated the dataset with the FMR setup, color information is not used and each fragment is subsampled to contain 10,000 points. To evaluate our method, adopting the metrics from DCP [33], mean square error (MSE), root MSE (RMSE) and mean absolute error (MAE) for rotation and transformation are used. For the FMR training setup, the results on 7 Scenes and 3 DMatch datasets are given in Table 1. As can be seen from the results, using only a limited amount of training data with both \(ALReg_{DIV}\) and \(ALReg_{UNC}\), better results are obtained than random selection for 7 Scenes.