SlowGuess/NeurIPS_2023_OCR huggingface dataset:
8) More Ablation Studies. We conducted more ablation studies in Table A7. We applied position encoding to input query point coordinates before high-dimensional feature embedding. We can observe that the resulting performances on the two complex meshes dragon and heptoroid are improved in different degrees, demonstrating the potential of exploring more advanced position encoding strategies. We also explored different ways of formulating the learning objectives in terms of the choice of distance metric. For example, we can choose to use  $L_{2}$  loss instead of  $L_{1}$  to calculate all learning objectives. Besides, for the formulation of shortest path supervision (i.e., formulated as Eq. (8) in the paper), we can also choose the popular Chamfer distance to supervise the curve deformation process. We can observe that in most cases using  $L_{2}$  loss causes different degrees of performance degradation, and Chamfer distance leads to further performance improvement. Still, in our implementation, we did not adopt Chamfer distance as our final choice because of its additional computational burden and memory cost.