Weaknesses
- The evaluation is missing some relevant recent methods for optimization-based surface reconstruction. (See details below)
- Some relevant related work is not cited. (See details below)
- The exposition is missing motivation for some design choices and is hard to understand in some parts. (See details below)
Overall, even though the authors missed some related work, results look quite promising and I think the idea is novel and interesting enough to make me lean towards acceptance. The exposition can probably be improved somewhat in the final version.
Details:
- The field of surface reconstruction from point clouds is quite vast, so the authors missed several works, for example (see also related work within these papers to get more missing work):
-Training-free methods:
- Neural-Singular-Hessian: Implicit Neural Representation of Unoriented Point Clouds by Enforcing Singular Hessian, Wang et al., TOG 2023
- Iterative Poisson Surface Reconstruction (iPSR) for Unoriented Points, Hou et al., TOG 2022
- Learning-based methods:
- 3DShape2Vecset: A 3D Shape Representation for Neural Fields and Generative Diffusion Models, Zhang et al., TOG 2023
- Geoudf: Surface reconstruction from 3d point clouds via geometry-guided distance representation, Ren et al., TOG 2023
- Given good normals, surface reconstruction becomes much easier - the normal computation could be followed by Poisson reconstruction based on the normals for example. Therefore papers to compute oriented normals are relevant, such as:
- Orienting Point Clouds with Dipole Propagation, Metzer et al., TOG 2021
- Globally Consistent Normal Orientation for Point Clouds by Regularizing the Winding-Number Field, Xu et al., TOG 2023
- SHS-Net: Learning Signed Hyper Surfaces for Oriented Normal Estimation of Point Clouds, Li et al., CVPR 2023
- Several design choices are not motivated well enough:
- It is unclear to me why the SDF evaluated at Q_1 uses a feature y_1 as input that was computed from a query position Q_0, rather than a feature computed at query position Q_1. This is an interesting design choice that could use more discussion. An ablation would also be interesting: how would the method perform when re-evaluating h_0, ..., h_i at Q_i instead of Q_0 when computing y_i for f(Q_i, y_i) (in that case inputting Q_i a second time to the LSNN might also not be necessary)?
- In Eq. 6, the design choice of only scaling D_1 and D_2 with the error-based factor, but not D_3 needs to be motivated. Also, as Section 3.2 was written for a general number of layers N_L, it would be good to continue this generality in Section 3.3, if possible. How would Eq. 6 look for a general N_L., for example?
- In Eq. 7, the design choice for using the minimum over the three levels.
- The exposition is hard to understand in some parts and could be improved:
- Several passages are currently hard to understand and could use clarification. For example: "The is because Q_i locate at different scales in space,, making it difficult to simultaneously account for the optimization step sizes at different scales with the inconsistent gradient directions caused by continuous movement." Its possible to get a good guess at what the authors mean here, but it requires re-reading a few times and is not 100% clear even then. Partially this seems to be due to bad grammar, partially due to a somewhat convoluted formulation. Generally the paper text could use more passes to avoid these issues.
- In Section 3.1, it should be mentioned early on that the FFT module is based on (or at least very similar to) MFN [11]. Also, the exact differences between the proposed approach in Section 3.1 and the approach of MFN [11] should be discussed and clarified.
- In Section 3.3, it would be good to mention that the loss functions are based on GridPull [20]. Also, it would be good to discuss the exact differences to GridPull (I assume the adpation to the multi-scale setting is the main difference).
- The architecture of the Linear Sequence Neural Network (LSNN) is never described. If it is taken from existing work, this work should be cited, otherwise it needs to be described, at least in Appendix A.
- In the paragraph starting at line 112 and Eq. 4, it is unclear which weights are initialized according to this strategy (the omega_i in Eq. 1? The W_i in Eq. 3? The offset theta_i and b_i?), and which parameters are plotted in Figure 3 (the L_i are not defined clearly).
- In Eq. 4, \eta is not defined.
- Near line 138, the query point update should probably use Q_{i-1} everywhere on the right-hand-side (instead of Q_i, which is not known yet). Or alternatively, Q_{i+1} on the left-hand side.
- In Eq. 1, I don't follow why the predicted signed distance f(Q_1, y_1) is in the loss. Why should that be optimized to be 0? This should be clarified.
- in Eq. 1, ", i \in [1,I]" can be removed, since it is redundant with the subscript in the sum
- In the section starting at line 160, how is the target surface point q determined? This is described in the appendix, but should be shortly mentioned in the main paper as well.
- In Eq. 7, argmin should probably be min instead. Also, the "1 - ..." in both lines seems odd, should it only be in one of the lines (otherwise the loss would be *higher* the more similar the gradient directions are, instead of lower).
- Near Eq. 8, the authors mention that L_surf encourages the SDF at the *final* query point to be close to 0, but Eq. 8 does not show an index for Q. Is L_surf applied to Q_{N_L} only or to all Q_i? This should be clarified.
- Near line 202: Fig. 4.1 should probably be Fig. 5
- In Table 7, Step 5 should probably have CD_L2 of 0.0357 (not 0.357)
- SAL [15] is in the bibliography, but not referenced from Section 2.
- Citations [45] and [59] are duplicates.
- An inference time comparison between methods should be included.