Algorithm 2 Procedure for estimating the weights 1: procedure ESTIMATEWEIGHTS( Teacher,Student,V,D) 2:.V is the validation dataset and D is the teacher-labeled dataset 3: U, k d12 p |V|e 4: for every (x,y) V do 5: X (Confidence(Teacher(x)),Confidence(Student(x))) 6: if arg max(Teacher(x)) = arg max(y) then: 7: (p,distortion) (0,1) 8: else: B.1 The student's test-accuracy-trajectory In this section we provide extended experimental results that show the student's test accuracy over the training trajectory corresponding to experiments we mentioned in Section 3.1. Notice that in the vast majority of cases our method significantly outperforms the conventional approach almost throughout the training process. The student's test accuracy over the training trajectory using harddistillation corresponding to the experiments of Figure 4. See Section 3.1.2 The student's test accuracy over the training trajectory corresponding to the experiments of Figure 5. See Section 3.1.2 The student's test accuracy over the training trajectory corresponding to the experiments of Figure 7. See Section 3.1.3 The student's test accuracy over the training trajectory using hard-distillation (first row) and soft-distillation (second row) corresponding to the experiments of Figure 8. See Section 3.1.4 Indeed, it is known (see e.g.

In this material, we first describe the importance of action validity prediction networks. Then, we introduce the details of the benchmarks, provide the model architecture, and present the additional experimental results, which are missing in the main article. We present the results of wall-clock time for computing the ground-truth action validity in Figure s.1. It shows that computing the action validity for a combination of 100 bricks needs more than 20 seconds. Moreover, we summarize the comparisons between possible action validation approaches as shown in Table s.1.0

In this section, we describe in detail the proposed architecture and its implementation. Our architecture is composed by an encoder and a decoder. The encoder receives as input a predefined number of learnable latent probes LP, together with the point coordinates of the target point cloud XT. Each layer of the encoder performs an operation of cross-attention between LP and XT followed by a self-attention on LP. Each attention is followed by a feed-forward layer.

In this paper, we propose a transformer-based procedure for the efficient registration of non-rigid 3D point clouds. The proposed approach is data-driven and adopts for the first time the transformer architecture in the registration task. Our method is general and applies to different settings. Given a fixed template with some desired properties (e.g.