Title: BeyondMix: Leveraging Structural Priors and Long-Range Dependencies for Domain-Invariant LiDAR Segmentation
Venue: NeurIPS 2025
Accepted: yes
Keywords: LiDAR  Segmentation

Unsupervised Domain Adaption
Abstract: Domain adaptation for LiDAR semantic segmentation remains challenging due to the complex structural properties of point cloud data. While mix-based paradigms have shown promise, they often fail to fully leverage the rich structural priors inherent in 3D LiDAR point clouds. In this paper, we identify three critical yet underexploited structural priors: permutation invariance, local consistency, and geometric consistency. We introduce BeyondMix, a novel framework that harnesses the capabilities of State Space Models (specifically Mamba) to construct and exploit these structural priors while modeling long-range dependencies that transcend the limited receptive fields of conventional voxel-based approaches. By employing space-filling curves to impose sequential ordering on point cloud data and implementing strategic spatial partitioning schemes, BeyondMix effectively captures domain-invariant representations. Extensive experiments on challenging LiDAR semantic segmentation benchmarks demonstrate that our approach consistently outperforms existing state-of-the-art methods, establishing a new paradigm for unsupervised domain adaptation in 3D point cloud understanding.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses unsupervised domain adaptation (UDA) for LiDAR semantic segmentation by improving upon existing mix-based strategies. The proposed method, BeyondMix, introduces a framework that explicitly models three structural priors: permutation invariance, local consistency, and geometric consistency. These priors are used to preserve intrinsic structure in point cloud data across domains. To enhance the model’s ability to capture long-range dependencies, BeyondMix leverages State Space Models, specifically Mamba, applied to sequentially ordered point cloud representations via space-filling curves. Experiments on SynLiDAR to SemanticKITTI and SemanticPOSS benchmarks show that BeyondMix achieves state-of-the-art cross-domain segmentation performance compared to prior methods.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n1. The paper precisely identifies the limitations of existing mix-based UDA methods for LiDAR segmentation, particularly their underutilization of structural priors and limited receptive fields. It then formulates a method that directly addresses these issues through principled design.\n2. BeyondMix introduces three underexplored structural priors of permutation invariance, local consistency, and geometric consistency formalized as loss functions. These are combined with a state-space sequence model (Mamba) using space-filling curves, enabling long-range context modeling across unordered point clouds.\n3. The method achieves state-of-the-art results on two challenging UDA benchmarks (SynLiDAR → SemanticKITTI and SemanticPOSS), with significant mIoU gains across multiple semantic classes and domains.\n4. The paper includes extensive ablations showing the individual and combined benefits of each structural prior and Mamba integration, supporting the effectiveness and complementarity of its components.\n\n**Weaknesses:**\n1.  The method involves non-trivial additions (Mamba, multiple loss terms), but the paper does not report training/inference time or memory usage, leaving concerns about practical efficiency unaddressed.\n2.   All experiments are from synthetic to real. The paper does not examine real-to-real domain adaptation or more diverse shifts (e.g., different sensor types), which narrows its generalization claims.\n3.  The ablation study reports only overall mIoU, without showing per-class performance. This prevents analysis of which semantic classes benefit from each structural prior.\n4. The main text lacks sufficient discussion of alternative long-range modeling methods, particularly recent transformer-based architectures like PointTransformer v3. These are only briefly mentioned in the appendix. The absence of conceptual or empirical comparison weakens the justification for using Mamba and diminishes clarity on architectural novelty.\n\nQUESTIONS:\n1. What are the runtime and memory overheads introduced by BeyondMix, particularly due to Mamba and the multiple auxiliary losses? \n2. Have you tested or considered BeyondMix under real-to-real domain shifts (e.g., SemanticKITTI → SemanticPOSS)? Would the same priors and Mamba integration apply effectively?\n3. Could you report per-class IoU in the ablation studies? This would help determine whether specific object categories benefit more from certain structural priors.\n4. Could you clarify how Mamba is integrated into your architecture (e.g., at voxel level or raw sequence)? Additionally, how does your use of Mamba compare in motivation and performance to recent long-range modeling methods such as PointTransformer v3? A conceptual and empirical contrast would help clarify the choice.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduce BeyondMix, which harnesses the capabilities of State Space Models (specifically Mamba) to construct and exploit these structural priors while modeling long-range dependencies. It tries to transcend the limited receptive fields of conventional voxel-based approaches. The suggested method has been validated to be effective.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**  \n1. This paper introduce BeyondMix, which leverages Mamba to construct structural priors beyond the capabilities of existing mix-based methods while simultaneously exploiting long-range dependency modeling to transcend limited voxel receptive fields for enhanced domain-invariant representation learning.  \n2. This paper introduces the Mamba model and solves the problem of different feature expressions caused by uneven LiDAR density to a certain extent by constructing geometric consistency priors.  \n**Weaknesses**  \n1. The permutation invariant prior module only selects two specific sorting sequences and learns to make their representations in the Mamba space approach each other, rather than making their representations similar in any sorting. Therefore, whether it can be called \"permutation invariant\" remains to be considered.  \n2. The meaning of figures 1 (b) and (c) is unclear, especially the table in (c) is difficult to understand.\n\nQUESTIONS:\n1. The Mamba model in the paper is only used as an auxiliary supervision module during the training phase. Has consideration been given to directly decoding Mamba features for classification during inference? Will this design have better results.  \n2. The receptive field at point B in Figure 2 contains two types of domains, but line 76 in the text states that they are not included;\n\nLIMITATIONS:\nThe authors discussed their limitations of their work.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces BeyondMix for UDA in LiDAR semantic segmentation. The main contribution is the utilization of a State Space Model (Mamba) to process point cloud features. By employing space-filling curves (e.g., Hilbert and Z-order curves) to serialize 3D point clouds, the method satisfies Mamba's requirement for sequential input. They also construct 3 structural priors: Permutation Invariance, Local Consistency, and Geometric Consistency. In addition, the authors propose leveraging Mamba's powerful long-range dependency modeling capabilities to capture context from across the entire point cloud scene.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n﻿\n1. The paper identifies two main weaknesses in existing methods: \"insufficient use of structural priors\" and \"limited receptive fields.\" To address these limitations, the paper proposes using the Mamba model to unify the solutions to both problems—modeling long-range dependencies and constructing new structural priors. \n﻿\n2. The paper resolves the challenge of processing unordered point clouds with Mamba by using space-filling curves and further leverages different serialization and partitioning schemes to construct priors. This design is highly innovative.\n\n3. The paper is well-written and easy to understand. The results also demonstrate the effectiveness of the proposed method.\n﻿\n﻿\nWeaknesses\n﻿\n1. To construct the various structural priors, the method requires multiple serializations of the features and repeated forward passes through the Mamba model. This may introduce considerably higher computational complexity than baseline methods. The paper lacks a quantitative analysis of runtime evaluation (e.g., inference time and FLOPs).\n\n2. It's common for UDA methods like DACS, DAFormer, and DGT-ST to use a combination of source loss (L s) and mixed-sample loss (L mix). If L mix is already being used to adapt to the target domain, why is the separate target loss, L t, also necessary?.\n\n3. The Mamba model captures long-range dependencies along the 1D path (\"flatten\" a 3D structure into a 1D sequence), but its ability to capture complex 3D spatial relationships that do not lie on this path may be weaker. This could lead to the loss of critical spatial information.\n\n4. The method enforces \"equivariance\" on intermediate features by inverse-mapping the Mamba outputs back to their original order. However, the ultimate goal for segmentation is to achieve \"invariance\" in the final prediction. \n\n5. The proposed Permutation Invariance and Local Consistency priors all work by altering the processing order of points and then enforcing consistency on the feature representations. It seems to be be conceptually overlapping.\n\nQUESTIONS:\nI would ask the authors to please provide a response addressing the weaknesses I have outlined to help resolve my concerns. If the authors can clarify these issues in their rebuttal, I will consider raising my score.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

comments: SUMMARY:
This paper proposes BeyondMix, a novel framework that harnesses the capabilities of State Space Models (specifically Mamba) to construct and exploit these structural priors while modeling long-range dependencies that transcend the limited receptive fields of conventional voxel-based approaches.

STRENGTHS AND WEAKNESSES:
Strength:
1. This paper identifies three critical yet underexploited structural priors: permutation invariance, local consistency, and geometric consistency.
2. Excellent work. The experiment is complete and sufficient. Writing and drawing are clear and easy to understand. 

Weakness:
1. Some multi-modal UDA methods for point cloud segmentation also needs to be discussed in related work.

QUESTIONS:
1. Figure 1(c) is confusing. What's the difference between the symbol "-" and "x"? Could the author provide a detailed explanation? It would be best for the author to provide the strategies adopted by the specific method.
2. Line 247, BeyondMix++ randomly selects from various existing mixing strategies. Could the author present the result of each mixing strategy? And which strategy best fits the proposed BeyondMix.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 4
IMPACT: 4
ORIGINALITY: 4
DATE: 2025-07-02