Title: NopeRoomGS: Indoor 3D Gaussian Splatting Optimization without Camera Pose Input
Venue: NeurIPS 2025
Accepted: yes
Keywords: Indoor Reconstruction; Pose-Free Gaussian Splatting;
Abstract: Recent advances in 3D Gaussian Splatting (3DGS) have enabled real-time, high-fidelity view synthesis, but remain critically dependent on camera poses estimated by Structure-from-Motion (SfM), which is notoriously unreliable in textureless indoor environments. To eliminate this dependency, recent pose-free variants have been proposed, yet they often fail under abrupt camera motion due to unstable initialization and purely photometric objectives. 
In this work, we introduce **Nope-RoomGS**, an optimization framework with no need for camera pose inputs, which effectively addresses the textureless regions and abrupt camera motion in indoor room environments through a local-to-global optimization paradigm for 3DGS reconstruction. In the local stage, we propose a lightweight local neural geometric representation to bootstrap a set of reliable local 3D Gaussians for separated short video clips, regularized by multi-frame tracking constraints and foundation model depth priors. This enables reliable initialization even in textureless regions or under abrupt camera motions. 
In the global stage, we fuse local 3D Gaussians into a unified 3DGS representation through an alternating optimization strategy that jointly refines camera poses and Gaussian parameters, effectively mitigating gradient interference between them. Furthermore, we decompose camera pose optimization based on a piecewise planarity assumption, further enhancing robustness under abrupt camera motion. Extensive experiments on Replica, ScanNet and Tanks & Temples demonstrate the state-of-the-art performance of our method in both camera pose estimation and novel view synthesis.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents NopeRoomGS, a novel *pose-free* 3D Gaussian Splatting (3DGS) framework that overcomes the limitations of traditional 3DGS methods, which rely heavily on Structure-from-Motion (SfM) for accurate camera pose estimation. SfM often fails in indoor environments with textureless surfaces or abrupt camera motions. NopeRoomGS addresses these challenges using a local-to-global optimization strategy. In the local stage, it bootstraps reliable 3D Gaussians from short video clips using a lightweight neural geometric representation, guided by multi-frame tracking and depth priors from foundation models. In the global stage, it fuses the local Gaussians and jointly optimizes both camera poses and Gaussian parameters using an alternating optimization scheme to reduce gradient conflicts. Additionally, it enhances robustness by decomposing camera optimization under a piecewise planar assumption. The method achieves state-of-the-art results on multiple benchmarks in both pose estimation and view synthesis.\n\nSTRENGTHS AND WEAKNESSES:\nStreath:\n1. The paper is well-structured and clearly written, making it easy for readers to understand and follow.\n\n2. This paper targets the poor performance of existing pose-free 3DGS frameworks in indoor textureless regions and under abrupt camera motion, and proposes a solution that demonstrates superior accuracy and robustness compared to existing methods.\n\n\n\nWeakness:\n1.  Lack of discussion and comparison with monocular Gaussian SLAM methods. Existing monocular Gaussian SLAM[1][2] approaches are also capable of recovering camera poses and reconstructing 3D Gaussian models using only RGB input. Based on the results reported in this paper, the proposed method does not seem to offer an advantage in terms of rendering quality or pose accuracy compared to monocular Gaussian SLAM[2].\n2.  The significance and ablation of the local neural geometric representation are not well justified. The paper employs a lightweight depth estimation network as a parametric representation of geometry, but does not provide a clear explanation of the rationale behind this design choice. Moreover, the ablation study on this aspect is incomplete. The authors should compare the results obtained using optimizable per-pixel depth variables (initialized with the predictions from the depth estimation network). Only through such comparison can the benefit of using a network to parameterize depth be properly demonstrated.\n3.  Lack of efficiency reporting. The paper should report optimization time and memory consumption, especially the overhead introduced by using a network to parameterize depth.\n\nQUESTIONS:\n1. Would it be possible to include a comparison with methods such as VGG-SfM, VGG-T, or DROID-SLAM, particularly in terms of pose accuracy? Notably, VGG-SfM also employs multi-frame tracking constraints similar to those used in this work.\n\n\n\n2. Did the authors observe any failure cases during the experiments?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-19"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nTraditional 3D reconstruction methods for indoor scenes often rely on Structure from Motion to estimate camera trajectories and scene geometry. NopeRoomGS introduces a new approach that avoids this dependency entirely. It uses a two-stage local-to-global optimization framework. In the first stage, it combines a pretrained monocular depth model like RepDepth with a visual tracker such as CoTracker to estimate local geometry and camera poses. In the second stage, it refines the entire Gaussian scene representation by enforcing multiview consistency and applying structural constraints such as planar surfaces. Experiments on standard indoor datasets like ScanNet and Replica show that NopeRoomGS outperforms existing methods including CF-3DGS and NopeNeRF in reconstruction quality.\n\nSTRENGTHS AND WEAKNESSES:\nThis work proposes a method for 3D Gaussian Splatting without relying on SfM or ground-truth poses. The pipeline is clear and well-structured, combining monocular depth and tracking for local geometry, followed by global pose refinement using structural priors. The design is practical, and the method outperforms several pose-free baselines. Some ablation results support the effectiveness of key components.\n\nHowever, the term “pose-free” is questionable. The method depends on pretrained models like RepDepth and CoTracker, which encode strong geometric priors. It would be more accurate to describe it as weakly supervised rather than truly pose-free. The system is also fairly complex, involving multiple non-end-to-end modules. The paper lacks details on resource usage, efficiency, and code availability, which raises concerns about reproducibility and deployment.\n\nQUESTIONS:\n1. The method relies on pretrained models like RepDepth and CoTracker, which carry strong geometric and motion priors. It’s unclear whether this still qualifies as “pose-free” under your definition. Please clarify.\n\n2. The appendix includes ablation results without RepDepth and CoTracker, but it would be helpful to test their replaceability and robustness. Try using simpler or non-learned alternatives, or simulate degraded performance, to better understand how sensitive the system is to prior quality.\n\n3. Please consider releasing the code, or at least providing an anonymous test script. Also, include a table comparing resource usage (e.g., memory, training time) with CF-3DGS on the same datasets, and indicate where the main computational bottlenecks are.\n\n4. The local-to-global structure is said to improve robustness in low-texture and fast-motion scenarios, but this isn’t well explained. Some theoretical reasoning or targeted experiments would help clarify why this setup works better under such conditions.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, to address the challenge of processing textureless regions and abrupt camera motion in pose-free 3DGS methods, the author proposes NopeRoomGS, a pose-free 3DGS framework with a local-to-global strategy. In detail, the authors split an image sequence into several video clips. For each clip viewed as a local stage, the camera pose and depth are jointly optimized. With the optimized depth, the 3D Gaussians can be initialized. In the global stage, the local 3D Gaussians will be fused into the global set of 3D Gaussians, and then the 3D Gaussians and camera poses will be optimized together in the global stage. The authors introduce a novel pose constraint based on piecewise planarity assumption to achieve more robust pose estimation. The experiments in several datasets show the effectiveness of the proposed framework.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper is well-written and easy to follow.\n2. The authors analyze the distributions of low-texture regions, camera trajectory curvature, and camera rotation angle difference across three benchmark datasets, and then figure out the fundamental challenges of textureless regions and abrupt camera motion. This analysis is meaningful and provides ideas on the bottleneck in the recent pose-free 3DGS methods.\n3. A novel pose constraint based on piecewise planarity assumption is proposed to enhance the robustness of camera pose estimation.\n4. The local-to-global strategy ensures the local geometric consistency, which leads to a better initialization for 3D Gaussians.\n\nWeakness:\n1. For each video clip, the authors use a monocular depth estimator to estimate depth for each image in the local stage. I would like to know how to ensure the optimized depths in the local stage have the same scale as the depths in the global stage, since in the fusing part, the scale of the location of local 3D Gaussians should align with the global 3D Gaussians. We know that the jointly optimized camera poses and depths in the local stage are missing a scale to the real scene, then for different video clips, there will be different scales for each clip. Therefore, I would like to know how to figure out this issue.\n2. It would be better to have memory and time comparisons with previous methods.\n3. It would be better if the authors could explain the difference between the 3DGS-based SLAM methods and the pose-free 3DGS methods, and provide some quantitative comparisons.\n\nQUESTIONS:\nPlease refer to the weakness part.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces NopeRoomGS, a pose-free indoor 3D Gaussian Splatting framework that that does not require Structure-from-Motion (SfM)-based camera pose initialization (similar in the settings to Nope-NeRF and Colmap-free 3DGS (CF-3DGS), etc.). The paper proposes a local-to-global optimization strategy to address textureless regions and abrust camera motion. It first estimates local pose and depth from short video clips using a pre-trained depth estimation foundation model, then globally refine these using photometric, depth and geometric objective functions, including a planarity-based pose regularizer. As a result, the paper conducts experiments on 3 public datasets, Tanks and Temples, Replica, and ScanNet to demontrate that the proposed method achieves SOTA performance.\n\nSTRENGTHS AND WEAKNESSES:\n**Paper Strengths**\n\na.\tCompared to previous methods that jointly optimize poses and NeRF/3DGS, the paper shows that adding local depth/pose estimation/optimization could lead to better rendering and pose refinement results\n\nb.\tThe ablation studies are reasonably clear and show effectiveness for each component.\n\nc.\tThe paper is clearly written and easy to follow, including well-presented figures and tables. I think this is worth giving credit for.\n\n**Weaknesses**\n\nI appreciate the authors’ effort to tackle an important research area, which is the joint optimization of camera poses and 3D scene representation learning. However, I have several concerns that I believe limit the impact of the current submission.\n\na.\tDespite in the relative work section, the paper states that existing novel view synthesis methods still rely on pre-computed COLMAP poses; it does not convincingly demonstrate that NopeRoomGS overcomes these limitations in practice. The proposed method is evaluated on Replica, ScanNet, and selected scenes from Tanks and Temples (please correct me if I’m wrong), which are datasets previously shown to be at least partially solvable by prior works such as BARF, Nope-NeRF, and CF-3DGS. To more rigorously support the claim of being a valid SfM/Pose-free alternative, I believe it should also be evaluated on sequences where COLMAP performs reliably. For example, why not test and report results on All Tanks and Temples scenarios, rather than selecting “advantageous” scenes that “work” like in Nope-NeRF? I think the comparison could be more thorough and show the full picture of the existing methods.\n\nb.\tI think the scope of the paper, which mainly focuses on challenging textureless indoor scenes, is narrow compared to real-life use cases. It is unclear if the system is just designed and tuned for indoor pose-free reconstruction. In addition, the proposed local-to-global optimization, seems to be very similar to GS-based SLAM methods, (to name a few: [1], [2], [3], etc.), which also estimate camera poses jointly with 3D Gaussian representations, and many are faster than CF-3DGS or Nope-NeRF. Adding comparisons (for example, on Scannet/Replica) and discussions of these methods would help position NopeRoomGS more clearly within the existing literature. It would be useful to articulate what capabilities NopeRoomGS provides that GS-based SLAM methods do not.\n\nc.\tThe idea of alternating optimization strategy, while proven to be effective in experiments, isn’t entirely new. Existing feature-based SfM methods like [4] also deploy this type of optimization strategy. However, it would be great if the paper could show a deeper analysis of why alternating optimization is particularly suited to the proposed setting.\n\nd.\tI find the ScanNet evaluation lacks important experimental details. It wasn’t clear which scenes were tested on ScanNet. A more detailed description of the experiment setting could improve transparency and reproducibility. \n\ne.\tThe proposed local-to-global approach adds algorithmic complexity, but the paper does not provide any runtime or memory usage analysis. It would be helpful to include those for better clarity.\n\nf.      Another relevant method to this work is [5], which seems to have better results in Tanks & Temples.\n\n[1] Li24 GS3LAM: Gaussian Semantic Splatting SLAM, ACMMM\n\n[2] Li24 SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM, ECCV \n\n[3] Yan24 GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting, CVPR\n\n[4] Brachmann25 Scene Coordinate Reconstruction: Posing of Image Collections via Incremental Learning of a Relocalizer, ECCV\n\n[5] Ji25 SfM-Free 3D Gaussian Splatting via Hierarchical Training, CVPR\n\n**Minor Weaknesses**\n\nIn Line 326, I’m not sure local neural geometric representation (LNGR) is a good name. How does the representation differ from depth? In addition, it is not introduced or explained in the method section.\nIn L127-129, “However, existing pose-free methods typically assume small pose perturbation … making them susceptible to failure under large camera motion or in textureless regions.” This claim here may not fully reflect the current state of the field, given that methods like Splatt3r [5] and InstantSplat [6] can handle relatively large camera motion differences with sparse input. I think this is worth double-checking. \n\n[5] Smart24 Splatt3r: Zero-shot gaussian splatting from uncalibrated image pairs\n\n[6] Fan24 InstantSplat: Sparse-view Gaussian Splatting in Seconds\n\nQUESTIONS:\n1. I think it would be helpful to provide evidence to support the pose-free claim.\n\n2. Also, direct comparison vs. GS-based SLAM method and discussion should be added.\n\n3. More insights on alternating strategy would be appreciated.\n\n4. The rest of the major and minor weaknesses.\n\nI do think there are interesting merits in the paper, for example, there are strong performance improvement in Replica.\n\nI would encourage the authors to address the concerns in the rebuttal. I also remain open to the possibility that I may have overlooked certain aspects\n\nLIMITATIONS:\nYes. The paper mentioned the high computational demands may contribute to higher carbon emission.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-23"}