Title: NoiseSDF2NoiseSDF: Learning Clean Neural Fields from Noisy Supervision
Venue: NeurIPS 2025
Accepted: no
Keywords: Noise2Noise;Neural Field;SDF;Point Cloud;Surface Reconstruction
Abstract: Reconstructing accurate implicit surface representations from point clouds remains a challenging task, particularly when data is captured using low-quality scanning devices. These point clouds often contain substantial noise, leading to inaccurate surface reconstructions. Inspired by the Noise2Noise paradigm for 2D images, we introduce NoiseSDF2NoiseSDF, a novel method designed to extend this concept to 3D neural fields. Our approach enables learning clean neural SDFs directly from noisy point clouds through noisy supervision by minimizing the MSE loss between noisy SDF representations, allowing the network to implicitly denoise and refine surface estimations. We evaluate the effectiveness of NoiseSDF2NoiseSDF on benchmarks, including the ShapeNet, ABC, Famous, and RealWorld datasets. Experimental results demonstrate that our framework significantly improves surface reconstruction quality from noisy inputs.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces NoiseSDF2NoiseSDF, a novel method that extends the Noise2Noise paradigm to 3D neural implicit representations, specifically signed distance functions (SDFs). The core idea is to learn clean SDFs from noisy point cloud data by supervising with other independently noisy SDFs, without requiring access to clean ground-truth data. The method exploits the coordinate consistency of neural SDFs, enabling the use of simple MSE loss for denoising. Built on top of the 3DS2V framework, the approach shows significant improvements in surface reconstruction quality under varying levels of noise across multiple datasets.\n\nSTRENGTHS AND WEAKNESSES:\nI find the Noise2Noise paradigm particularly appealing, and extending it from 2D images to 3D point clouds and further into SDF fields is a well-motivated and elegant direction. The adaptation to SDFs is intuitive and well-justified, leveraging their coordinate-aligned structure to enable effective supervision. While the network architecture and design choices are relatively simple and may not appear highly novel at first glance, I believe this is a valuable and meaningful attempt. The method is practical, requiring no clean labels, and remains agnostic to the specific Point2SDF backbone, making it broadly applicable. The overall pipeline is clearly described and thoughtfully implemented.\nHowever, the method's reliance on pretrained Point2SDF networks (e.g., 3DS2V) as supervision raises questions about how much the final performance depends on the quality and noise sensitivity of those networks. Does the method still work when Point2SDF predictions are especially inconsistent? How sensitive is the denoising effect to architectural choices or training regimes of the supervising model?\nWhile the paper compares with several baselines, it lacks a quantitative and visual comparison with Noise2Noise Mapping[1]—a key representative of Noise2Noise in the point cloud domain with open-source code available. A direct comparison, e.g., on a small dataset like Famous, would more clearly highlight the benefits of applying Noise2Noise in the SDF space over point-wise methods.\n\n[1] Baorui Ma, Yu-Shen Liu, and Zhizhong Han. Learning signed distance functions from noisy 3d point clouds via noise to noise mapping. In Proceedings of the 40th International Conference on Machine Learning (ICML)\n\nQUESTIONS:\nSee weaknesses.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a new approach for learning clean SDF from noisy point clouds, producing robust surface reconstruction. The key idea is to treat the 3D queries as the 2D pixels for a noise-to-noise learning. The results outperform several baselines, demonstrating the effectiveness of proposed method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. The paper is well written. Figure 1 clearly shows the key insight of the proposed method, making the idea easy to understand.\n2. The idea is simple but works well. The insight is new which solves the difficulty in 3D point matching by learning with correspondences of 3D queries.\n\nWeaknesses\n1. The experiments should be improved. Some SOTA methods are not included in the comparison, which makes it hard to evaluate the performance of proposed method. For example, Neural IMLS, Noise2Noise Mapping, LocalN2NM. I understand that some of those methods are optimization-based, while a comparison is also recommended since all those methods are in the same setting. \n2. What if we first clean the noisy point clouds with point denoising methods (e.g. IterativeFPN) and then reconstruct surfaces from the cleaned points? Will the proposed method still performs better?\n3. The authors only show results under object-level models. Can the proposed method be used in reconstructing scene-level models with noisy scene point clouds as input?\n4. The title of the figures and tables should not be centered, please check the format provided by the official.\n5. Missing references to some new works. Fast Learning of Signed Distance Functions from Noisy Point Clouds via Noise to Noise Mapping [TPAMI24]. Noise4Denoise: Leveraging Noise for Unsupervised Point Cloud Denoising [CVM 24].\n\nQUESTIONS:\nPlease refer to the weaknesses above. I am overall not negative with the proposed method, my main concerns are with the insufficient experiments. I am willing to raise my score if my concerns are well addressed.\n\nLIMITATIONS:\nYes. In the appendix.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper tackles the task of reconstructing implicit neural surfaces from noisy point cloud primitives by extending the Noise2Noise paradigm to Signed Distance Functions (SDFs). The entire framework operates under the mathematical premise that the observations of a given signal with an independent, zero-mean noise converge to the actual signal. The original Noise2Noise method demonstrated the effectiveness of utilizing noisy observations as supervisory signals with pixel-wise correspondence in the image denoising task. Instead of seeking point-wise correspondence brutally when moving from images to point clouds, the authors leverage the power of implicit neural surfaces to utilize the coordinates of SDF when building the correspondence required for the denoising procedure.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. The proposed method offers a novel insight into surface reconstruction from noisy point clouds, which has potential applications in various fields.\n\n2. The idea of leveraging coordinate correspondence in SDF as a workaround for finding point-wise correspondence directly between two given noisy point clouds is innovative and technically sound.\n\n3. According to the experimental results, the proposed method has noticeable improvements over the existing methods in both quantitative and qualitative evaluations.  \n\n**Weaknesses**\n1. While the proposed method demonstrates quantitative improvements over previous approaches, the qualitative results (e.g., Figure 4) indicate that this method struggles to reconstruct shapes with high-frequency details and thin structures.\n\n2. The authors claim that the proposed method does not impose limitations on the architecture of SDF prediction network (the Point2SDF part). The manuscript itself does not include any related ablation study to support this generalization ability.\n\n3. The authors utilize the encoder and decoder structure proposed by 3DS2V, and also include 3DS2V in their comparison, and in my understanding, the comparison is made using the shape autoencoding pipeline from 3DS2V. The proposed method has two noisy point cloud inputs, while the 3DS2V pipeline only sees one noisy point cloud. How to keep the comparison fair when input information is not balanced between different methods.\n\nQUESTIONS:\n1. See weaknesses.\n\n2. While the authors include the source code of the proposed method in the supplementary material, I could not find either the Point2SDF part in the implementation or the exact method name of the used method. It would be better to elaborate on the architecture and method for completeness.\n\n3. Regarding the experiment settings, is there any limitation on how each method can be evaluated on different datasets? If not, why is the 3DS2V method included in both Tables 1, 2, and 3 while some other methods are absent in some experimental settings?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper tackles the problem of implicit surface reconstruction from sparse noisy point clouds. It proposes a method to finetune the decoder of a generalizable model (point-to-SDF model) by minimizing the squared difference between the sdf fields obtained by conditioning on two different noisy version of the same input point cloud. The effectiveness of method is evaluated by training and testing on the ShapeNet dataset.  The generalization capability of the method is assessed by training on the train set of the ABC dataset and evaluating on the Famous and Reak datasets.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\nThe paper is well written and easy to follow.\n- The proposed method is clearly presented and motivated as a rigorous application of the  *Noise2Noise* approach to Signed distance functions.\n- The proposed method outperforms it’s baseline across datasets and metrics.\n\n## Weaknesses\n- **Missing Comparaison and discussion:**\n    - w.r.t generalizable models NKSR, NoKSR, FS-SDF\n    - w.r.t to recent single shape optimisation methods especially those dedicated to sparse and noisy point clouds  (NTPS, SparseOcc, Noise2Noise Mapping [30])*\n    - w.r.t Noise2Noise Mapping [30]: I think comparaison to this baseline is important as it introduces the noise2noise paradigm for SDF learning and shows very competitive results to generalizable methods. \n- **Motivation and ablations:**\n    1. Why using   noisy-target supervision in neural SDFs for surface reconstruction is good idea ? I thing to answer this question some ablations are needed* \n        1. The proposed loss  function can be seen as encouraging the model the be invariant to the noise on the input point cloud.  How does the method compare to direct sdf supervision  using the same batch configuration (same batch size , same number of shapes per batch, etc)\n        2. Why is the loss used to finetune the decoder but not the encoder: Instead of finetuning the decoder such as that it produces the same sdf using latent features from different noisy versions of the same input why not simply apply the noise2noise loss to the encoder’s output + direct sdf supervision of the decoder ?\n- Experiments on non object centric datasets/ large scale datasets (Faust , Scannet, Synthetic Rooms) would strengthen the paper.\n\nQUESTIONS:\n- I didn't understand what the authors mean by \"To ensure that all SDF targets are consistently noisy, we freeze its parameters during this process\". Does it mean  the pretrained 3DS2V model is fixed  during the whole training. Or you just detach the SDF prediction corresponding to p1 from the computational graph ?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-06"}