Title: U-CAN: Unsupervised Point Cloud Denoising with Consistency-Aware Noise2Noise Matching
Venue: NeurIPS 2025
Accepted: yes
Keywords: Unsupervised point cloud denoising

noise-to-noise matching

denoising consistency
Abstract: Point clouds captured by scanning sensors are often perturbed by noise, which have a highly negative impact on downstream tasks (e.g. surface reconstruction and shape understanding). Previous works mostly focus on training neural networks with noisy-clean point cloud pairs for learning denoising priors, which requires extensively manual efforts. In this work, we introduce U-CAN, an Unsupervised framework for point cloud denoising with Consistency-Aware Noise2Noise matching. Specifically, we leverage a neural network to infer a multi-step denoising path for each point of a shape or scene with a noise to noise matching schema. We achieve this by a novel loss which enables statistical reasoning on multiple noisy point cloud observations. We further introduce a novel constraint on the denoised geometry consistency for learning consistency-aware denoising patterns. We justify that the proposed constraint is a general term which is not limited to 3D domain and can also contribute to the area of 2D image denoising. Our evaluations under the widely used benchmarks in point cloud denoising, upsampling and image denoising show significant improvement over the state-of-the-art unsupervised methods, where U-CAN also produces comparable results with the supervised methods. Project page: https://gloriasze.github.io/U-CAN/.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces  U-CAN , an unsupervised framework for point cloud denoising that leverages a  Consistency-Aware Noise2Noise Matching  approach. U-CAN employs a neural network to infer multi-step denoising paths for noisy point clouds, using a novel  Noise to Noise Matching loss  to learn denoising patterns without requiring clean ground truth data. Additionally, it introduces a  Denoising Consistency loss  to ensure geometric consistency across multiple noisy observations, enhancing robustness. The framework is evaluated on synthetic (PUNet) and real-world (Paris-rue-Madame) datasets, achieving state-of-the-art performance in unsupervised point cloud denoising and comparable results to supervised methods. U-CAN also demonstrates applicability to 2D image denoising (McMaster18 dataset) and unsupervised point cloud upsampling, showcasing its versatility.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The paper introduces U-CAN, an unsupervised framework that leverages consistency-aware Noise2Noise matching to denoise point clouds without clean supervision. By using a neural network to infer multi-step denoising paths and a novel loss function for statistical reasoning on noisy observations, U-CAN overcomes the need for manually annotated clean point clouds, addressing a key challenge in prior supervised methods . The framework's design, which adapts Noise2Noise to 3D point clouds via Earth Mover's Distance (EMD), effectively handles the unordered nature of point sets.\n\n2. The proposed Denoising Consistency Constraint (L_DC) ensures geometric consistency across denoised predictions from different noisy inputs, mitigating ambiguity in unsupervised learning. This constraint not only improves local geometry preservation in point clouds but also generalizes to 2D image denoising, demonstrating its versatility . The combination of L_N2N (Noise to Noise Matching loss) and L_DC enables U-CAN to produce high-fidelity results while maintaining computational efficiency. \n\n3. U-CAN outperforms state-of-the-art unsupervised methods in point cloud denoising, upsampling, and image denoising. \n\nWeaknesses :\n\n1. Unverified Effectiveness with Alternative Backbone Networks\nThe study focuses on a specific denoising network architecture but does not explore how the proposed Consistency-Aware Noise2Noise losses (Lₙ₂ₙ and L_DC) perform when integrated with other backbone networks (e.g., PointNet++,  or transformer-based architectures). Verifying their generality across different network designs is crucial to confirm the framework’s adaptability and robustness.\n\n2.Lack of Evaluation on Indoor Point Cloud Datasets\nThe paper does not assess U-CAN on indoor point cloud benchmarks such as ScanNet, which are critical for real-world applications in robotics, AR/VR, and autonomous interior navigation. Indoor scenes often contain complex structures (e.g., furniture, partitions) and varying noise distributions (e.g., sensor reflections or occlusions), posing unique challenges for denoising.\n\nQUESTIONS:\nSee weakness.\n\nLIMITATIONS:\nThe authors adequately address limitations. They note the qualitative nature of evaluations on real-world data (Paris-rue-Madame) due to the lack of ground truth  and acknowledge challenges in predicting high-fidelity local geometries compared to supervised methods. The paper appropriately marks societal impact questions as “NA” (not provided in the excerpt but assumed per NeurIPS guidelines), as the work is foundational and does not involve high-risk applications. The transparency about limitations is commendable, and no critical points appear to be missing.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-11"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes an unsupervised multi-step point cloud denoising network, which leverages a noise-to-noise matching loss and a geometric consistency loss to guide training. The proposed approach achieves competitive performance in unsupervised denoising tasks.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**: \n- The paper is well-organized and easy to follow.\n- The topic of unsupervised point cloud denoising is meaningful.\n- Enforcing consistency among denoised results from different scans of the same object/scene is a reasonable and insightful strategy.\n\n**Weaknesses**: \\\nMajor Concerns:\n- 1. The motivation and core framework of this paper closely resemble the *Noise2NoiseMapping* approach proposed in [1]. Several key statements are similar with the phrasing of [1]. For example:\\\nline 39 of this paper: \"**The key idea** of this noise to noise matching is to leverage the **statistical reasoning** to reveal the clean structures upon its several noisy observations\" \\\nline 140: \"The correspondences support the key assumption of Noise2Noise that **the noisy values at the same pixel location of different observations are random realizations of a distribution around a clean pixel value.**\" \\\nWhile sharing similar motivations with prior work can be acceptable, the proposed solution itself also exhibits notable similarities. Both this paper and [1] achieve unsupervised denoising by ensuring that the denoised output of a noisy scan aligns closely (in terms of EMD) with other scans of the same object/scene. Notably, the use of EMD as a loss metric was presented as a contribution in [1], and has since been adopted in other works such as [2].\n- 2. The multi-step denoising design should not be claimed as a contribution (line 54), as similar multi-stage frameworks have been explored since [3]. Does the proposed multi-step denoising framework apply a loss function at each step for supervision? Prior studies have shown that applying supervision at each stage can enhance denoising performance.\n- 3. The experimental section does not include a comparison with [1], which is a highly relevant baseline.\n- 4. As shown in Table 1, the proposed method performs worse in terms of P2M under low noise levels compared to moderate or high noise. This is counterintuitive since low-noise data should be easier to denoise. Could the authors provide an explanation for this?\n\nMinor Concerns:\n- 1. The related work section misses several recent fully-supervised denoising methods proposed after [3]. Even though the paper focuses on unsupervised methods, a brief discussion of these approaches would provide better context.\n- 2. Lines 110 and 116 use the word \"schema,\" which appears to be a typo and should be corrected to \"scheme.\"\n- 3. Please clarify what the symbol P denotes in Equation (1).\n- 4. In Figure 5, the proposed method appears to over-smooth some structural details, particularly on flat surfaces such as walls. This might be a common issue in unsupervised methods, and the authors have mentioned this limitation. Still, it would be helpful to give some possible solutions.\n\n[1] Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping\\\n[2] Denoising Point Clouds in Latent Space via Graph Convolution and Invertible Neural Network\\\n[3] IterativePFN: True Iterative Point Cloud Filtering\n\nQUESTIONS:\nPlease refer to the \"weakness\" section mentioned above.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-14"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper extends Noise2Noise in the field of 2D image denoising to 3D data, and achieves high-quality point cloud denoising through the proposed \"Noise to Noise Matching\" and \"Denoising Consistency Constraint\".\n\nSTRENGTHS AND WEAKNESSES:\nThe structure is clear, and the proposed U-CAN can achieve data denoising across multiple modalities with good performance.\n\nQUESTIONS:\n1. This experiment adds various noises to the clean PUNet dataset to synthesize network inputs. I wonder if the noise distributions of Pa and Pb are consistent. In other words, are Pa and Pb obtained by adding the same level of noise to the same clean shape/scene, but with different noise distributions?\n2. If the noise distributions of Pa and Pb are different, I believe there is a risk of data leakage. Since Pa and Pb are contaminated with noise in different areas, the network could directly learn from the complementary regions of Pa and Pb without any denoising.\n3. I also noticed that the authors added real-world noise to PUNet for their experiments. I would like to know how U-CAN performs in denoising single-frame point clouds that contain noise in the real world.\n4 . This work contains two branches, Pa->Ca and Pb->Cb. I am curious whether the networks of these two branches are two independent networks or the same network with shared weights? Compared with existing unsupervised algorithms, does U-CAN have an advantage in model complexity?\n5. U-CAN contains two parallel denoising branches. Does this greatly increase the inference time of the model? I hope the author can provide relevant data.\n\nLIMITATIONS:\nThe core idea of this work comes from Noise2Noise for 2D image denoising, but I didn't see any relevant content in the introduction of the manuscript.\nThe latest comparison algorithm in this work is from 2021, which makes it difficult to explain the superiority of U-CAN.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces U-CAN, a novel unsupervised framework for denoising 3D point clouds without needing clean reference data. [24] The method trains a neural network using a \"Noise-to-Noise\" matching scheme, where the goal is to make the denoised version of one noisy point cloud resemble another noisy observation of the same object. [25, 54] Key to its success is a new loss function based on Earth Moving Distance (EMD) to establish point correspondences, and a \"Denoising Consistency\" constraint that ensures predictions from different noisy inputs remain consistent. The authors demonstrate that U-CAN significantly outperforms state-of-the-art unsupervised methods and achieves comparable results to supervised techniques in point cloud denoising, upsampling, and even 2D image denoising.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength:**\n- The work introduces U-CAN, an innovative unsupervised framework that learns to denoise point clouds without requiring pairs of noisy and clean data, which are often difficult and expensive to create.\n\n- The core concept of denoising consistency is shown to be a general principle that can be transferred to improve unsupervised 2D image denoising as well.\n\n- The paper is well presented with solid experiment results.\n\nQUESTIONS:\n- The choice of Earth Mover's Distance (EMD) is well-justified by its ability to find one-to-one correspondences, which is critical for the Noise-to-Noise matching loss. However, EMD is known to be computationally expensive. Can you give a rough number on the computational and training time of the denoise model.\n\n- Can you clarify how are different noisy observations P_a and P_b generated? \n\n- Since U-CAN performs an 1-1 matching between noisy and denoised point clouds (PC), I am curious how are the noisy points mapped to the denoised ones. Would the cluster of noisy points mapped into their closest point from the denoised PC?\n\nLIMITATIONS:\nSee the questions.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}