SlowGuess/NeurIPS_2023_OCR huggingface dataset:
As an extension of Fig.4 in the manuscript, Fig.8 illustrates the learning progress of the temporal similarity map during the training phase for different values of  $k$  ( $k = 1,2,3$ ). As the training progresses, the cross-modal temporal similarity map  $\mathbf{S}^{(-k)}$  becomes similar to the intra-modal temporal similarity  $\mathbf{T}^{(-k)}$ . This indicates that the student model effectively learns the temporal knowledge encoded in the similarity maps from the teacher, and ultimately leads to significant improvements in 3DOD, as demonstrated in our extensive experiments reported in the manuscript.
Figure 5: Examples of 3D object detection results from LiDAR-based model (top-left), multi-view camera-based model with STXD (top-middle), and camera-only model (top-right). Green rectangles indicate ground truth bounding boxes, while blue rectangles represent the predicted bounding boxes. Pink dashed boxes highlight the comparisons of STXD with the LiDAR-based model, while red dashed boxes highlight the comparisons with the camera-only model. We also provide camera views corresponding to the highlighted regions at the bottom.
Figure 6: Examples of 3D object detection results from LiDAR-based model (top-left), multi-view camera-based model with STXD (top-middle), and camera-only model (top-right). Green rectangles indicate ground truth bounding boxes, while blue rectangles represent the predicted bounding boxes. Pink dashed boxes highlight the comparisons of STXD with the LiDAR-based model, while red dashed boxes highlight the comparisons with the camera-only model. We also provide camera views corresponding to the highlighted regions at the bottom.
(c) MSE w/ GT
(d) MSE
Figure 7: Examples of BEV features generated by (a) the LiDAR-based model, (b)-(d) the multi-view camera-based model trained with different feature-level distillation methods, and (e) the camera-only model. We visualize the  $\mathcal{L}_2$ -norm value of BEV features.
(e Camera
Figure 8: Examples of the learning progress for the temporal similarities during the training phase.