Title: High Resolution UDF Meshing via Iterative Networks
Venue: NeurIPS 2025
Accepted: yes
Keywords: unsigned distance fields

udf

meshing

surface extraction

neural implicit representations
Abstract: Unsigned Distance Fields (UDFs) are a natural implicit representation for open surfaces but, unlike Signed Distance Fields (SDFs), are challenging to triangulate into explicit meshes. This is especially true at high resolutions where neural UDFs exhibit higher noise levels, which makes it hard to capture fine details.

Most current techniques perform within single voxels without reference to their neighborhood, resulting in missing surface and holes where the UDF is ambiguous or noisy. We  show that this can be remedied by performing several passes and by reasoning on previously extracted surface elements to incorporate neighborhood information. Our key contribution is an iterative neural network that does this and progressively improves surface recovery within each voxel by spatially propagating information from increasingly distant neighbors. Unlike single-pass methods, our approach integrates newly detected surfaces, distance values, and gradients across multiple iterations, effectively correcting errors and stabilizing extraction in challenging regions. Experiments on diverse 3D models demonstrate that our method produces significantly more accurate and complete meshes than existing approaches, particularly for complex geometries, enabling UDF surface extraction at higher resolutions where traditional methods fail.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors present a framework that reconstruct the mesh from a learned unsigned distance field (UDF) network. Given a learned UDF network, this paper iteratively query the UDF network on a grid and enforces sign configuration consistency with its neighboring cells. Doing so helps close the holes due to UDF networks usually cannot reach true zero in their UDF predictions.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. The authors present an improvement over various previous works. There is a catch though, see below.\n\n**Weaknesses**\n1. The authors use an autoencoder to learn the UDF field, and such choice is subject to serious question. For example in the teaser image (Figure 1), the authors claim that neural UDFs often fail to reach zero. While this is often the case, the quality of such learned UDF field should not be bad enough to fail to reconstruct the nosewheel of the aircraft. This brings the question of the intrinsic quality of the learned UDF in this paper. The authors follow the architecture of DeepSDF dating back to 2019. In recent years there have been multiple UDF learning methods -- CAP-UDF [1] (the learning part), GeoUDF [2], LevelsetUDF [3], DUDF [4], DEUDF [5], just to name a few. It is unclear whether the bad results (from CAP-UDF, DCUDF, etc) shown in this paper are the fault of the mesh extraction methods or the learned UDF field, and if the latter is the case, the comparison of the paper is unfair. While in supplementary material the authors present another UDF network setup (Section A.5), it is still an autoencoder.\n2. I am also very concerned about the authors' misrepresentation of other works. Specifically, the authors claim that GaussianUDF [6] also follows the autoencoder framework used in this paper to represent the UDF (L188; suppl. L46-47), which seems completely untrue -- their network follows NeuralUDF [7], which is a simple MLP with positional encoding. See their paper Section 4.1 \"Details\" \"The UDF $f$ ... 8-layer MLP with 256 hidden units ... apply positional encoding ...\"\n\n**Others**\n1. The paper heavily uses the term \"nerual UDF\", but since there is a well-known work literally named \"NeuralUDF\" [7], it only adds to the confusion.\n\n[1] Zhou et al. \"CAP-UDF: Learning Unsigned Distance Functions Progressively from Raw Point Clouds with Consistency-Aware Field Optimization\" (NeurIPS 2022 / TPAMI)\n\n[2] Ren et al. \"GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation\" (ICCV 2023)\n\n[3] Zhou et al. \"Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projection\" (ICCV 2023)\n\n[4] Fainstein et al. \"DUDF: Differentiable Unsigned Distance Fields with Hyperbolic Scaling\" (CVPR 2024)\n\n[5] Xu et al. \"Details Enhancement in Unsigned Distance Field Learning for High-fidelity 3D Surface Reconstruction\" (AAAI 2025)\n\n[6] Li et al. \"GaussianUDF: Inferring Unsigned Distance Functions through 3D Gaussian Splatting\" (To appear in CVPR 2025)\n\n[7] Long et al. \"NeuralUDF: Learning Unsigned Distance Fields for Multi-view Reconstruction of Surfaces with Arbitrary Topologies\" (CVPR 2023)\n\nQUESTIONS:\nI cannot accept the paper with an inherent unfair comparison. If the authors could demonstrate the UDF extraction results with more advanced UDF learning methods, and show that they exhibit better qualities over other UDF mesh extraction methods, I would reconsider my rating.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 1, "ORIGINALITY": 2, "DATE": "2025-06-07"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a new approach for meshing neural Unsigned Distance Fields (UDFs) at high resolution, which are typically more difficult to triangulate than Signed Distance Fields due to the lack of sign changes near the surface. The proposed method introduces an iterative refinement strategy: instead of performing surface extraction in a single pass, the network operates over multiple iterations, progressively improving local predictions by integrating surface information from neighboring voxels. At each iteration, the network predicts pseudo-sign configurations for the vertices of a voxel using UDF values, gradients, and surrounding context. This iterative propagation scheme enables the recovery of finer surface details, closes holes, and stabilizes extraction in noisy or ambiguous regions. The approach is tested across diverse 3D datasets and resolutions, demonstrating improved robustness over existing methods.\n\nSTRENGTHS AND WEAKNESSES:\nThe proposed method tackles a well-known challenge in high-resolution UDF meshing by introducing a conceptually simple yet powerful idea: iterative correction based on spatial propagation. This naturally complements the limitations of single-pass algorithms and effectively exploits structure within the field. The design of the network is well-explained and integrates neighboring context without needing complicated geometry-specific heuristics. Additionally, the method is robust to noise and generalizes across multiple datasets and resolutions, showcasing the practicality of the approach for real-world high-resolution meshing tasks.\nSeveral points, however, could be clarified further. First, the approach currently uses a fixed maximum number of iterations (up to 6), selected empirically. Could the authors provide insights into whether an adaptive stopping criterion might be beneficial, or whether performance saturates early? Second, while the filtering strategy for removing high-UDF cells is described, it's unclear whether the model remains robust under different UDF scales or UDFs learned from different network architectures—some discussion on generalizability across UDF sources would be helpful.Third, some related work such as “Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projection” could be cited for completeness.\n\nQUESTIONS:\nsee weaknesses\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces an iterative scheme to compute pseudo-signs for Unsigned Distance Fields (UDF) mesh extraction. The core idea is to progressively refine the result by taking into consideration the previous interaction pseudo-signs, UDF values and gradients.\n\nSTRENGTHS AND WEAKNESSES:\nTo be honest, I don't see noticeable flaws in the paper, so I will focus on the strengths.\n\n## Strengths\n\n* Excellent motivation. The problem and the reasons why it exists are clearly described. The claims are properly stated and are sound.\n\n* Contextualization is done well. The differences in comparison with related work are discussed in detail. The authors present confident knowledge in the neural UDF literature.\n\n* Good writing. The paper reads well and is easy to follow. The math notation is also good.\n\n* Experiments provide an excellent overview of how the area deals with mesh extraction. The results are adequate for the claims: a method designed to work well on high resolutions, avoiding the noise generated by the difficult of achieving true zeros at the trained UDF.\n\n* About the method itself, its design makes sense. The iterative nature is aligned with the idea of considering the current neighborhood while computing the pseudo-signs. This seems to be a good alternative to analytic approaches, such as computing curvature, for example, which would also provide neighborhood analysis but would be influenced by gradient existence on discontinuities at the surface. I also liked the efforts into making it faster and more robust to noise. Being an iterative procedure, ensuring it has comparable running times to other SOTA methods is important for it to be considered as a viable choice. Noise robustness is also very important because of the focus on high-resolution reconstruction. The worries of the authors are on-point with the necessities of the problem at hand. \n\n## Current assessment\n\nI am not completely familiar with all the current neural UDF literature. I have reviewed a few papers on recent conferences in the subject, but I am more familiar with SDFs. It is possible that my rating decreases in case reasonable important flaws are pointed by the other reviewers. If that is not the case, I recommend acceptance.\n\nQUESTIONS:\nNo additional questions.\n\nLIMITATIONS:\nYes, they are described in Section 5.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the problem of mesh extraction from neural unsigned distance fields (UDFs). Its main contribution is an iterative technique that leverages neighborhood information from the marching cubes grid to refine surface extraction through multiple passes of a neural network. The approach is shown to perform well on complex and high-resolution shapes.\n\nThe method builds upon NSD-UDF, with significant overlap in the methodology section; which would benefit from further polishing. This raises the question of how incremental the contribution is, especially since the main novelty (iterative refinement scheme) is introduced without any theoretical justification. This should be clearly acknowledged in the introduction.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nThe claim in [L45] that meshing models should incorporate information from previously extracted surface elements is compelling. The evaluation confirms that the proposed iterative refinement improves reconstruction quality. However, the paper lacks a theoretical explanation for why this approach works.\n\nThe authors conduct extensive experiments demonstrating improved reconstructions compared to the state of the art.\n\nWeakness:\n\n[L21] The paper cites DeepSDF as a primary motivation for using SDFs in INRs. However, a more accurate motivation involves the use of the Eikonal equation for regularization. Missing references include:\n- Sitzmann et al., Implicit Neural Representations with Periodic Activation Functions, NeurIPS\n- Gropp et al., Implicit Geometric Regularization for Learning Shapes, ICML\n- Novello et al., Exploring Differential Geometry in Neural Implicits, Computers & Graphics\n- Fainstein et al., DUDF: Differentiable Unsigned Distance Fields with Hyperbolic Scaling, CVPR\n\nThe sentences in [L28–30] and [L33–34] appear redundant and should be revised for clarity.\n\n[L35] “worsens the problem, as shown in the results section” — Please indicate the exact section number or figure that supports this claim.\n\n[Figure 1] This figure needs refinement. Consider showing the full 3D model with zoom-ins to improve clarity. Also, please include the ground-truth mesh for comparison. \n\n[Section 3.2] This section appears to be mostly implementation details.\n\nQUESTIONS:\n[Figure 5] Why do the reconstructions appear voxelized? Please clarify.\n\nSince the proposed method does not depend on the UDF being represented by an INR,  how does it perform when applied to the ground-truth UDF?\n\nMost of the reconstructed surfaces shown are low in detail. Can the method be applied to more detailed models, such as the Thai Statue and the Asian Dragon?\nNote that these cases would require marching cubes at a resolution higher than 512×512.\n\nRegarding the augmentation term. It appears to be a design choice inherited from NSD-UDF, which showed that this factor improves reconstruction quality. However, given the introduction of a refinement step in the current method, I am curious whether this term is still necessary or beneficial.\nFinally, I was also curious about the dataset split. Did the authors divide the data into training and validation sets and report results only on the validation set? If so, could they clarify how this split was performed?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}