NopeRoomGS: Indoor 3D Gaussian Splatting Optimization without Camera Pose Input
Authors: Wenbo Li
Yan Xu
Mingde Yao
Fengjie Liang
Jiankai Sun
Menglu Wang
Guofeng Zhang
Linjiang Huang
Hongsheng Li
Venue: NeurIPS 2025 poster ()
Keywords: Indoor Reconstruction; Pose-Free Gaussian Splatting;
Primary area: applications

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.

NopeRoomGS: Indoor 3D Gaussian Splatting Optimization without Camera Pose Input
Wenbo Li
Yan Xu 1.3.3 Mingde Yao 1.2 Fengjie Liang*
Jiankai Sun
Menglu Wang
Guofeng Zhang' Linjiang Huang Hongsheng Li
*MMLab, The Chinese University of Hong Kong CCPI University of Michigan
'Hong Kong Polytechnic University Stanforrd USTC "Zhejiang University *BUAA
wenboli@zju. edu. en, yxumich@umic ch. ,du,
zhangguofenggezj...ddu.c 1 ljhuang@buaa. edu.en, en, hslißee, cuhk edu.
Ours
CF-3DGS
Figure 1: Comparison of camera pose estimation and novel view synthesis with the state-of-the Compared to CF-3DGS [10] (bottom), our method (top) achieves more robust pose estimation and more photorealistic novel view synthesis in challenging indoor seenes with textureless regions or abrupt camera motion. Each example contains the camera trajectory estimation (left) and sampled synthesized view (right).
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 Atructure-from-Moote
(SfM), which is notoriously unreliable 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 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 under abrupt camera motions. In the global stage, we fuse local 3D Gaussians into unified 3DGS representation through 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 piecewise planarity assumption, further enhancing robust- ness under abrupt camera motion. Extensive experiments on Replica, ScanNet and
-ocal-to-global
in textureless regions
Co-first authorship. Corresponding author.
39th Conference on Neural Information Processing Systems (NeurIPS 2025).

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Tanks & Temples demonstrate the tfae-of--hhe-rr performance of our method in both camera pose estimation and novel view synthesis.
1 Introduction
The reconstruction of 3D representations from images for photorealistic novel view synthesis is longstanding challenge in computer vision and graphics. It has broad applications in augmented reality, virtual reality, and robotics. Recent advances in learning-basse have significantly improved rendering fidelity and generalization. However, the majority of these approaches still rely on externally estimated camera poses, typically obtained via Structure-from Motion (SfM) pipelines [35, 49]. This dependency introduces critical vulnerability: SIM is notoriously unreliable in low-texture regions and under non-smooth camera motion [12, 13, 32], where sparse or ambiguous feature matches lead to pose estimation failures. Such inaccuracies corrupt reconstruction optimization. but also degrade synthesis quality [38, 18, 42, 7]. entangling final novel view synthesis quality with the success or failure of an external, heuristic-heavy preprocessing step.
methods [35, 2, 49, 51, 25, 31]
This misalignment has motivated efforts to re- move SfM entirely and jointly optimize pose and scene representation [5, 31, 53, 62, 20, 10J. However, this joint optimization problem presents classic chicken-and-egg dilemma, re- quiring careful algorithmic design. BARF [31] addresses this by employing progressive fre- quency scheduling strategy, gradually increasing the Fourier components in NeRF optimization to stabilize camera pose estimation while pre- serving accurate scene reconstruction. Nope- NeRF [5] introduces framr-frame geometric con- straints and enhances the pose representation to improve stability. More recently, 3D Gaus- sian Splatting (3DGS) [25] has emerged as a powerful alternative to NeRF, offering both higher rendering efficiency and superior visual quality. Building on this representation, Fu et al. [10] propose the first framework for pose-free novel view synthesis. To facilitate convergence, their method incrementally grows 3D Gaussians frame by frame the camera moves, optimizing each frame's 's pose solely through photometric error minimization.
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The distributions of low-texture regions (A), camera trajectory curvature (B). and camera rota- tion difference (C) vary significantly across different scenarios: Tanks & Temples [27]. Replica [44], and ScanNet [9]. The indoor datascts, Replica and ScanNet, contain more textureless regions and exhibit more abrupt camera motions than Tanks & Temples, which is gen- erally texture-rich and captured under more controlled conditions. To create more challenging scenarios, we further mask textured regions and sample frames with larger temporal steps Tanks & Temples. Under these settings, our method demonstrates strong robustness large textureless areas and sudden camera motions, while Nope-NeRt NeRF [5] and CF-3DGS [10] show notable perfor- mance drops (D, E).
Figure 2:
Despite significant advancements, existing pose- free methods often assume that the photomet- ric error from visual textures alone can provide strong and reliable gradients for accurate pose optimization. However, this assumption fre- quently breaks down in the presence of texture- less regions or abrupt camera, e.g., in indoor environments. We evaluated several state-of-the-art methods on indoor datasets and observed notable performance degradation (see Tab. 1 for details). Furthermore, when we artificially mask texture-rich regions or increase the camera motion speed within the datasets originally used by these methods, we observe similarly pronounced performance drops (Fig. 2). These findings empirically validate our hypothesis regarding the limitations of the current approaches in indoor environments.
To overcome these limitations, we propose NopeRoomGS, a robust pose-free 3DGS framework designed for scenarios where textureless regions and abrupt camera motion prevail, especially indoor environments. Our method employs a oocl--t-gllobal scheme to progressively build up the 3DGS.
In the local stage, we introduce a lightweight local neural geometric representation that jointly optimizes depth and camera pose on short, overlapping video clips extracted from the input sequence. To recover the consistent geometry of each video clip, the optimization is supervised by multi- frame tracking constraints derived from CoTracker [23], which remain effective even under abrupt camera
2

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motion. Furthermore, to enhance robustness in textureless regions, we incorporate regularization from pretrained monocular depth foundation model [24]. This design enables reliable recovery of local 3D geometry and camera poses under challenging conditions with textureless regions and abrupt camera motion, thereby providing stable initialization of local 3D Gaussians for subsequent global fusion.
In the global stage, we progressively fuse these local 3D Gaussians into a unified global 3DGS representation over entire video sequence. This fusion process is formulated as an optimization problem, where the global 3DGS is supervised by combination of photometric loss, depth alignment loss, and pose constraints derived from piecewise planarity assumption, ensuring global geometric consistency. Furthermore, we adopt an alternating optimization strategy that updates Gaussian parameters and camera poses in turn, rather than jointly, to reduce gradient interference and enhance convergence stability. This strategy improves the robustness of global reconstruction in challenging scenarios with textureless regions and abrupt camera motion.
Our contributions are summarized follows:
We propose a pose-free 3DGS framework with a local-to-global scheme, which exhibits strong robustness in textureless regions and under abrupt camera motion.
In the local stage, we introduce lightweight neural geometric representation that jointly optimizes depth and camera pose over short video clips. It yields locally consistent geometry and accurate poses, even textureless regions and under abrupt camera motion, effectively bootstrapping set of local 3D Gaussians.
In the global stage, we progressively fuse local 3D Gaussians into a unified global 3DGS representation with alternative optimization strategy. To ensure stable convergence, we supervise the model with the combination of photometric loss, depth alignment loss, and pose constraints derived from piecewise planarity assumption. These components collectively enforce structural consistency in the global reconstruction.
Experiments on public datasets, including Replica [44] and ScanNet [9] and Tanks & Temples [27], demonstrate that our method achieves state-of-the-art performance, with particularly strong results in indoor scenes and competitive results in general scenarios.
2
Related Work
Novel view synthesis. Generating photorealistic images from novel viewpoints is key challenge in computer vision, which has been approached using diverse 3D representations. Among them, Neural Radiance Fields (NeRFs) [35] have emerged leading method, with extensions tackling challenges such as aliasing 3, 3, 4]. surface reflectance [48, 1J, sparse views [26, 37, 55, 60, 19]. In parallel, explicit point-based or mesh-based representations have gained increasing attention for their efficiency and interpretability [56, 65, 25, 34, 28, 64, 33J. In particular, 3DGS [25] demonstrates that real-time, high-fidelity view synthesis can be achieved by representing scenes as sets of anisotropic Gaussian primitives optimized through differentiable rendering. However, many of these methods still rely on pre-computed camera poses. Although the camera pose estimation has been studied for decades [36, 43, 58, 57, 47, 16, 17, 50]. it is still challenging to robustly estimate the camera poses with low-cost sensors. Most recent novel view synthesis methods rely on Structure- cenetturr-fro---ee from-Motion tools like COLMAP 1, 43, 36, 46, 33], which limit their applicability in scenarios where obtaining accurate camera poses is challenging, such in low-texture environments or when only sparse or unstructured image collections are available.
Radiance field without camera pose prior. Recent studies have integrated pose estimation into NeRF training to remove dependence on pre-computed camera poses. Early methods, such as i-NeRF [63], refined camera parameters by aligning keypoints to a pre-trained NeRF, while NeRFmm [54] jointly optimized both the NeRF model and camera poses, albeit with limitations to forward-facing scenes. BARF [31] addressed gradient inconsistency in positional encoding with coarse-to-fin fine optimization, but still required initial pose estimates within of the ground truth.
Nope-NeRF [5] leveraged monocular depth priors for both scene reconstruction and pose estimation. Despite these advances, NeRF-based methods remain computationally intensive and face challenges in achieving real-time rendering performance [40].
3DGS without camera pose prior. Recent work such as CF-3DGS [10] first removes COLMAP
dependency by jointly optimizing poses and Gaussians under inter-frame photometric supervision, making them susceptible to failure under large camera motion or textureless regions. In parallel, growing line of -oowwrd, pose-free free 3DGS methods [61, 22, 15, 8] propose generalizable
3

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Figure 3: The pipeline of NopeRoomGS. Our NopeRoomGS framework adopts a Local-to- to-ollaaall optimization scheme to address textureless regions and abrupt camera motion. In the local stage (Sec. 3.1), a lightweight Local Neural Geometric Representation jointly optimizes depth and camera pose for each clip, yielding consistent geometry and local 3D Gaussians. the global stage (Sec. 3.2), these are fused into unified 3DGS via differentiable optimization with photometric, depth, and pose constraints under the Piecewise Planarity Assumption. An Alternating Optimization strategy mitigates gradient interference, ensuring stable and accurate reconstruction.
networks to predict Gaussians (and often poses) without per-scene optimization. NoPoSplat [61] predicts 3D Gaussians directly in a canonical space, whereas SelfSplat [22] jointly learns camera poses and scene geometry via self-supervised depth and photometric consistency. Although these approaches remove the need for external SIM. they still face limitations when processing very large image collections, where computational and memory costs grow sharply and maintaining stable global pose/geometry estimates becomes challenging.
In contrast, we fully exploit 3D aumuctuaal-consitenn constraints and coudatiionmoode priors with a lightweight local neural geometric representation, and fuse the resulting local 3D Gaussians using alternating optimization strategy camera poses and 3D Gaussian parameters, which stabilizes ini- tialization, reduces gradient interference, and yields globally consistent reconstructions in challenging scenarios with textureless regions and abrupt camera motion.
3 Method
Given sequence of N unposed images I {1 # J Ji=1 with known camera intrinsic, goal is to recover the camera poses ' (T, E SE(3)) and reconstruct unified global 3DGS representation of the scene.
This task is fundamentally challenging because both accurate camera poses and reliable 3DGS representation must be recovered simultaneously from unposed inputs. The explicit nature of the 3DGS representation, though beneficial for efficient rendering, makes this joint optimization highly non-convex and sensitive to initialization [21, 6]. In particular, the problem is exacerbated in indoor environments characterized by textureless surfaces and abrupt camera motion, where standard feature- based methods fail and gradient descent is prone to local minima [14]. To improve robustness, we adopt local-to-globas optimization scheme, illustrated in Fig. 3. In the local stage (Sec. 3.1), introduce a learnable local neural geometric representation that jointly estimates depth and camera pose within each video clip. This yields reliable scene structure and camera pose to bootstrap set local 3D Gaussians as an initialization for global optimization, which is critical for stable convergence [59]. In the global stage (Sec. 3.2), these local 3D Gaussians are progressively fused into a unified global 3DGS representation. This fusion is formulated as differentiable optimization problem, supervised by combination of photometric loss, depth alignment loss, and pose constraints based on piecewise planarity assumption. In addition, adopt alternating optimization strategy to updates camera poses and Gaussian parameters in turn, rather than jointly [30], to mitigate gradient interference and enhance convergence.
3.1 Local Stage
In the local stage, to address the challenging scenario with textureless regions and abrupt camera motion, propose local neural geometric representation that jointly optimizes camera poses

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and per-frame depth via gradient descent. The optimization is supervised by multi-frame tracking constraints [23]. which enhance geometric coherence under abrupt camera motion. Additionally, we incorporate strong depth priors from pretrained foundation model [24] to regularize the solution in textureless regions. Unlike prior pairwise methods [10], our representation is shared across all frames within a clip, enabling consistent geometric reasoning across different viewpoints.
Local neural geometric representation. To ensure consistent geometry under challenging conditions such as textureless regions and abrupt camera motion, we parameterize the local scene structure using a lightweight local neural geometric representation Fo, where 0 denotes learnable parameters. To make this representation both robust and rapidly deployable, Fo is initialized from fast monocular depth estimator [4 I] and shared across all frames within short video clip, enabling coherent geometry over the video clip.
Given a short clip {I; E # of length L, the local neural geometric representation Fo takes as input a color image I; to predict the corresponding depth map Do.::
Do.; Fo().).
(I)
We denote the depth maps of the clip as DB = (Do. e E [HXWWL, Ji=1. By sharing the network Fø across the local video clip, consistent geometric cues can be learned during optimization. The intuition of this optimization process similar the classic bundle adjustment, but our approach is based on neural representations.
Joint optimization of local camera pose and depth. After obtaining the initial depth maps above, we jointly optimize the local camera poses T = (T; E SE(3) and the depth maps Da by enforcing geometric consistency across frames.
Specifically, for any pixel u, in the frame i, we can get its correspondences {uyli e (u,)) in the neighboring frames .' (u.) by using the quasi-dense off-the-shelf multi-frame tracking constraints derived from CoTracker [23]. To enforce the geometric constraints for pose optimization, first unproject the pixel u, to get its 3D position p, in the current frame:
Pi = - Do. (u:)KK
where K denotes the intrinsic matrix and De. (u,) its estimated depth value in Eq. 1. Then, we map it to the nearby frames and enforce the geometric consistency by minimizing the projection error:
Σ jEN(u.)
u,
where (-) denotes the pinhole camera projection function given the intrinsic matrix K, and Tz- E SE(3) denotes the relative pose transformation from frame to frame j.
To avoid the depth optimization deviate too far away from the realistic solution manifold, we regularize its prediction with the output from a stronger but much heavier monocular depth foundation model [24], striking balance between performance and efficiency. This regularization is implemented by a sat---nnn------inrraiii regularization loss [41]:
C = D.
where Do.; is the output from local neural geometric representation Fo. and D; is the pseudo ground-truth produced by the foundation model, and p(-) is distance function (i.e., Huber loss). The parameters a and 33 are obtained by solving least-squares problem [41] to resolve the scale ambiguity of monocular depth estimation.
The overall objective function in the local stage is thus:
T, D.Dg arg min Cpros + WLesss T.D.
(5)
where Epro and Casi are the summation of all the L/ and Cig respectively within the same clip, and the weight w controls the regularization strength. Here, we slightly abuse the notation Dg. as we optimize the parameter 8 practically.
5

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BARF
NoPe-NeRF
CF-3DGS
Ours
GT
Figure 4: Qualitative comparison of novel view synthesis on Replica [44] dataset. Compared with other cutting-edge counterparts, our method synthesizes more detailed textures under the pose-free setting.
3.2 Global Stage
After obtaining the local camera poses T and depth maps D3 within each clip via Eq. 5, initialize local 3D Gaussians accordingly and progressively merge them into unified global 3DGS representation G.
Specifically, for each frame i within clip, unproject its depth map Dg.; the 3D space using camera intrinsic K (Eq. 2) to get the point cloud P, and transform using the estimated pose T, to the local frame of this clip. After finishing this process, get a local point cloud for each clip. To decrease the memory burden, we downsample the point cloud and initialize the local 3D Gaussians with the downsampled point cloud. Then, the local 3D Gaussians are merged into unified global 3DGS representation according to the relative pose between the local frame and the global frame. Thereafter, we optimize the global 3D Gaussian parameters as well as the camera pose based on the input image I and estimated depth map D, from the local stage. In the ensuing part, will elaborate on the objective function we used for optimization.
3.2.1 Objective Function
Photometric loss. Following the original 3DGS [25], we include the photometric loss terms in the objective function:
Sip = (T.))T)) 111 + - ()DD))))
(6)
which is constituted by a L1 term and a D-SSIM term [25]. (T(:)) represents the rendered image from the Gaussian Splatting G with the camera pose T,, I; is the i-th input image, and y is the hyperparameter to balance the two terms.
Depth alignment loss. With the photometric loss, the camera poses can be effectively corrected by the regions with rich textures, as these regions can produce strong gradients if the poses are erroneous. However, the indoor scenes are full of textureless areas, e.g., blank walls, which pose great challenges pose-free 3DGS. To enhance the robustness in textureless regions, also add a depth alignment term similar to Eq. 4:
Clepth A -
The difference is that we here use the optimized depth maps Dø from the local stage as the ground truth constrain the depth maps Dg(T.) rendered with camera pose T..

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Table 1: Camera pose estimation performance metrics comparison Replica [44] dataset. All baseline methods are trained with their official implementations and original configurations, and evaluated following the same protocol to ensure fair and consistent comparison. The best results are highlighted in bold.
Scenes
Ours
CF-3DGS (10)
NoPoSpli [61]
SeliSplai [22]
Nepe-NeRF [5]
BARF [2]
NeRFiam [54]
RPE,
RPE.
RIPE:
RPE.
RPPE
RPE.
ATE
RPE.
RIPE:
RPE.
RPE,
ATE
office0
0.231
0.153
0.011
1.168
7,282
0.043
1.456
7.456
0.053
1,073
7,854
0.074
1.231
5,462
0.042
1.561
6.273
0.052
1.667
6.834
0.000
officel
0.120
0,645
0,081
1.171
6,288
0.078
1.325
5,946
0.067
0.946
8,678
0.064
1.415
7,922
0.056
1.462
5.234
0.048
1432
7.236
0.054
oflice?
0.404
012601
1.668
1884
7.764
0.053
1.554
0.074
5.234
6.235
1.34
0.03
office?
0.056
0.062
0.083
1.489
5.994
0.091
2.113
7.235
0.067
2,038
8.245
0.056
1.156
6,002
0.05)
1.119
7.345
0.0%6
14899
7,231
0.54
officed
0.083
0.126
0,083
1.085
6,901
0.048
1.922
7.567
0,072
1,807
7.113
0.054
0.923
6.231
0.037
1.225
5.987
0.041
1996
6,923
0.045
6.545
1834
1.504
5432
7,235
0.53
roml
0.230
0.175
6.961
2.325
0.043
2.164
6.883
0.095
6.256
0.025
1:045
5.436
0.043
1.322
6,933
0072
1o0m2
0.111
0.128
0.008
1.177
5.584
0.064
1.325
8,385
0.084
1.164
7.224
0.083
1.231
3,467
0.036
1,032
3.467
0.023
1.377
6,342
0.043
0.148
0.126
0.009
1.150
5.905
0.059
1.733
7,086
0.061
1.531
7.56
0.072
6.001
0.039
1.230
6.051
0.046
1523
6,962 0.048
Pose constraint based on piecewise planarity assumption. The camera motion could be abrupt in indoor scenes. To further improve the robustness against abrupt camera motion, we propose cross-frame geometric constraint based on a piccewise planarity assumption.
We assume each point x, in the scene lies where n; is the surface normal and 6; is the displacement coefficient. The normal and the displacement coefficient are calculated from the rendered depth map of 3DGS G, using 4 surrounding pixels (left, right, upper, lower).
infinitesimal piecewise plane defined by n. x, x,+d; d;
Given two consecutive frames, I, and 44+1 with relative pose estimation T Ti [R,-++11
(,45-11 1 between, the plane parameters in frame i, denoted (n,, 8,), can be transformed to frame
i 1 by
i:41 Ri Di
(8)
8,41 6c n. ti-ii1:
(9)
to get the transformed plane parameters (f;-1, 11+1)-
We then enforce consistency between the transformed plane parameters (f.,1, 8,-1) and the directly estimated values (n,-1, d1+1) in the frame + 1, using the following loss function:
C++1 plane
An |||111111 T 112 + Ao [8..1 6:+1/1
(10)
where X, and As are the regularization parameters that control the relative importance of normal and offset consistency, respectively. This loss function ensures that the geometries in consecutive frames coherently aligned and decomposes the supervision to rotational and translational components of the camera poses. We found that, by adjusting An and Åç. we can achieve more robust pose estimation in indoor environments. To further enhance robustness, we also extract the edge map based on input frame, which used as a mask to restrict this constraint only planar regions, while avoiding its effect on the plane edges. Further detail can be found in the supplementary materials.
Overall global optimization. By combining the objective functions mentioned above, the overall optimization process is formulated by summing over all the frames:
T.g arg min T&G
>xplane Splane + Aigh + Adeptt Caepphh
(11)
where the weights Aplane Augh, and Adepth balance different terms.
3.2.2 Alternating Optimization Strategy
Although Eq. 11 defines a unified objective over both the global camera poses T and the 3D Gaussian parameters G. jointly optimizing them often results in unstable convergence due to gradient interference between the two parameter spaces [21, 6, 14]. To mitigate this issue, we adopt an alternating optimization strategy, updating camera poses and 3D Gaussian parameters in turn. This decoupled scheme improves convergence stability and preserves global consistency, as validated by our ablation studies in Sec. 4.4. Further implementation details, including the gradient update formulation for camera pose parameters, provided in the supplementary material.
7

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Table 2: Novel view synthesis performance metries comparison Replica [44] dataset. For fair comparison, each baseline is trained using its publicly released code and original hyperparameter settings, and evaluated under the same protocol. The best results are highlighted in bold.
Sccnes
CF-3DGS [10)
NcPoSyjia [si]
Scliggui [2:]]
Napc-NeRFF
BARF (2)
NcRFFum [54]
PSNR
SSIM
LPIPSS
SSIM
LPIPS
PSNR
LPIS
PSNR
SSTM
LPISS
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
LPISS
effice
[.92
0.09
0.38
17:34
0.42
16.30
0.56
0.35
0.78
0:48
15:53
0.49
officel
31.78
8.92
0.10
29.11
0.55
0.18
18.32
0.41
0.41
18.11
0.50
0.40
27.35
0.60
0.40
26.35
0.63
0.53
25.33
0.61
0.53
emhoe
39.77
1.91
0.11
24,57
0.78
0.24
18.54
0.36
0.34
15.57
0:45
0.43
26.77
0.76
0.35
26.52
0.73
0.43
23:25
0.6
0.54
effice
32.37
1.90
0.12
23.50
0.75
0.25
15:84
0.54
0.65
21:20
0.57
0.65
36.01
0.34
36-37
061
0.41
15:25
0.7%
0.45
effice
0.99
0.89
1853
15:49
0.84
26.48
24.05
0.43
roont)
0.96
19.66
0.45
18.63
0.54
0.73
0.32
22:33
0.1
mmm1
32.18
1187
0.08
24.80
0.74
0.35
23:92
0.5%
0.46
22.15
0.48
0.43
29.43
0.30
0.18
0.56
0.54
15. 40
0.43
100m2
30.40
0.88
0.10
25.34
0.74
0.34
22.08
0.55
0.36
23.42
0.55
0.48
28.96
0.61
0.47
24:83
0.53
0.60
24.16
0.43
0,40
32.14
1..0
0.09
25.40
0.77
0.79
19.19
0.52
0.41
18.90
0.54
0.42
46
0.75
0.40
86
0.65
0:46
14.34
0.1
0.47
4 Experiment
4.1
Experimental Setup
Datasets. We evaluate our method on three public datasets: Replica [44], ScanNet [9], and Tanks & Temples [27], covering both synthetic and real-world scenes. The Replica [44] dataset offers high-fidelity synthetic indoor scenes with precise ground-trute camera poses. Its large textureless regions and complex camera trajectories make it well-suited for evaluating camera pose estimation and novel view synthesis. The ScanNet [9] dataset consists of real-world RGB-D indoor scenes captured in unconstrained environments, presenting challenges such as sensor noise and motion blur. Tanks& & Temples [27] dataset contains texture-rich scenes with relatively controlled camera motions. Most previous methods are evaluated on this dataset. We include it to test the generalizaiility of our method.
Metrics. Following prior works [10, 5], we evaluate our method on two key tasks: camera pose estimation and novel view synthesis. For camera pose estimation, we use standard visual odometry metrics [29, 45]. including Absolute Trajectory Error (ATE) and rotational Relative Pose Error (RPE,) and translational Relative Pose Error (RPEg). For novel view synthesis, we use standard image quality metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) [52], and Learned Perceptual Image Patch Similarity (LPIPS) [66].
Implementation Details. Our method is implemented using PyTorrh [39], building on the opti- mization settings from 3DGS [25] with necessary adjustments. A key feature is synchronizing new frame additions with point densification intervals to ensure steady scene expansion. For detailed information, please refer to the supplementary materials.
4.2 Replica
In this subsection, we conduct a comparative analysis of our method against several established baselines, including NoPoSplat [61]. SelfSplat [22], CF-3DGS [10], Nope-NeRF [5], BARF [2] and NeRFmm [54], all of which are widely recognized for their contributions to camera pose estimation and novel view synthesis.
For camera pose estimation, the optimized camera poses are aligned with Procrustes Analysis, as described in prior works [5, 54], and compared against the ground-truth poses from training views. Quantitative results summarized in Tab. 1, where our method achieves performance superior to current state-of-the-ar approaches.
For novel view synthesis, we adopt the evaluation protocol of CF-3DGS [10] and NeRFmm [54]. The optimized 3DGS model, trained exclusively on the training views, is kept fixed, while the camera poses of the test views refined by minimizing the photometric recon- struction error between synthesized and ground- truth images. As reported in Tab. 2, our method consistently outperforms all baselines. More- over, the qualitative comparisons in Fig. 4 high- light that our synthesized images preserve finer details and cxhibit sharper textures than those produced by existing methods.
Table 3: Camera pose estimation and novel view syn- thesis performance metries comparison on ScanNet [9] datasct. For fair comparison, each baseline is trained using its publicly released code and original hyperparam- eter settings, and evaluated under the same evaluation protocol. The best results are highlighted in hold.
Methods
RPE.
RPE,
PSNR
LPIPS
(54)
[3]
B.894
0.54
Nope-NarF (S)
03561
01686
0.040
12.11
0164
(11)
SelfSpar (22)
0.584
[1912
0.231
15.28
0.52
0.45
NoPoSplar (s1)
3,367
0.775
0.124
17.24
0.54
0.67
CF-SDGS [10]
0.633
0.684
0.040
25.36
0.68
0.24
Ous
0.579
0.524
0.020
25.38
0.72
0.30
8

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4.3 ScanNet
To further validate the effectiveness of our method in realistic and unconstrained environments, we evaluate it on ScanNet [9] dataset, which consists of real-world indoor scenes. Compared to Replica [44] dataset, ScanNet [9] dataset introduces additional challenges such sensor noise, clut- tered layouts, and motion blur, making it more demanding benchmark for pose and reconstruction quality. We follow the evaluation protocol of [10, 5] to assess both camera pose estimation accuracy and novel view synthesis quality. As shown in Tab. 3, our method consistently achieves superior results across all evaluation metrics.
4.4 Tanks& & Temples
While Replica [44] and ScanNet [9] datasets pro- vide indoor scenes, we further evaluate the gen- eralization ability and robustness of our method on Tanks & Temples [27] dataset, which con- sists of photogrammetric reconstructions of both indoor and outdoor scenes with texture varia- tions. Following standard evaluation protocols established in [10, 5], we assess both camera pose estimation accuracy and novel view synthe- sis quality. As reported in Tab. 4, our method performs competitively with state-of-the-art ap- proaches across nearly all metrics. Qualitative results are presented in Fig. 5.
Table 4:
Camera pose estimation and novel view synthesis performance metrics comparison Tanks & Temples [27] dataset. All baseline methods are trained with their official implementations and original config- urations, and evaluated following the same protocol to ensure fair and consistent comparison. The best results
are highlighted in bold.
Mashoak
RFE.
PSNR
(54)
0.61
L5g
0.006
26.34
0.74
SelfSpla (22]
1.045
0.480
0.094
22:42
0.58
0.56
3837
0.835
0.117
20:15
0.53
0.47
CF-SDGS [10]
0.041
0.069
0.004
31.28
0.93
0.09
Ours
6.834
0.043
0
31,68
0.94
0.07
4.5 Ablation Study
In this section, we analyze the effectiveness of different key components proposed in our pipeline through systematic ablation studies.
Effectiveness of local neural geometric repre- sentation. To evaluate the contribution of the local neural geometric representation (LNGR), conduct an ablation study where LNGR is removed and replaced with a naive initialization strategy: the camera pose of each frame is ini- tialized using that of the previous frame, and depth is directly estimated from monocular foundation model [24] without LNGR refinement.
CF-3DGS
Ouus
GT
Figure 5: Qualitative results of novel view synthesis on Tanks & Temples [27] dataset. Our NopeRoomGS produces more realistic rendering results than other base- lines.
As shown in Tab. 5 (row "w/o LNGR") and Fig. 6, this configuration causes a noticeable drop in both pose accuracy and rendering quality, which underscores the essential role of LNGR Fú in handling challenging indoor scenarios with textureless regions and abrupt camera motion. As described in Sec. 3.1, by jointly optimizing depth and pose across short video clips, Fa produces consistent and robust local scene geometry and camera pose. These outputs provide reliable initializations for local 3D Gaussians, which in turn supply high-quality geometry and pose priors for the subsequent global optimization stage. Such strong initializations crucial for ensuring stable convergence and achieving high reconstruction fidelity.
Effectiveness of piecewise planarity assump- tion. To assess the impact of the piecewise planarity assumption (PPA), we ablate the PPA constraint from the global optimization stage and replace with pairwise depth consistency loss computed via reprojection between adjacent frames.
Table 5: Ablation study on Replica [44] datasct. A comparison of our full pipeline and variants without Local Neural Geometric Representation (LNGR), Piece- wise Planarity Assumption (PPA), Alternating Optimiza- tion Strategy (AOS), and Depth Alignment Loss (DAL.) (described in Eq. 7), respectively.
Methods
RPE, J
RPE,
ATE..
PSNR ?
SSIM 1
LPIPS 1
As shown in Tab. 5 (row "w/o PPA") and Fig. 6, this substitution results decline in both cam- era pose accuracy and novel view synthesis qual- ity, thereby substantiating the efficacy of PPA in guiding global optimization. As detailed in Sec. 3,2.1, PPA improves camera pose estimation in textureless regions by introducing geometry-
ours
0.148
0.126
0.009
32.14
(1.90)
0.09
ENGR
1,552
7.422
0.063
0.42
0.179
0.023
wio AOS
0.205
0.189
0.019
78.28
0.89
0.15
wio DAL
0.156
0.158
0.078
31.42
0.90
0.14

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OusS
We Local Seutal Geometric
wis Plecewise Planarity Aoumpion
Wio Altemizinng Oyümmzzion
web Daych Aligtment
Representtion
Figure 6: Ablation study camera pose estimation and novel view synthesis on Replica [44] datasct. We compare our full pipeline with variants without local neural geometric representations, piecewise planarity assumption, alternating optimization strategy, and depth alignment loss as described in Eq. 7, respectively. The 3D trajectories are projected onto the XY plane of the coordinate system.
aware supervision, and stabilizes optimization under abrupt camera motion by decomposing the supervision signal into rotational (normal alignment) and translational (offset consistency) compo- nents, which contributes to the robustness of the overall reconstruction process.
Effectiveness of alternating optimization strategy. To evaluate the impact of our incremental and Alteraatin Optimization Strategy (AOS), we replace with joint global optimization method. As shown in Tab. 5 (row "w/o AOS") and Fig. 6, this substitution leads to drop in both camera pose accuracy and novel view synthesis quality, highlighting the effectiveness of proposed design. Given the same reliable initialization, alternating optimization plays a critical role in achieving stable convergence and higher reconstruction quality mitigating interference between camera pose and 3D Gaussian parameters updates.
Effectiveness of depth alignment loss. To assess the effectiveness of the Depth Alignment Loss (DAL) Eq. 7, we replace the optimized depth from the local stage with monocular predictions from a pretrained foundation model [24] to supervise the depth rendered from the global 3DGS stage. As shown in Tab. 5 (row "w/o DAL") and Fig. 6, this leads to degraded camera pose accuracy and novel view synthesis quality. These results highlight the importance depth alignment supervision and demonstrate the effectiveness of our local neural geometric representation in recovering consistent scene geometry.
5 Conclusion and Limitation
In this work, we propose NopeRoomGSS, fully pose-free (i.e., no pose priors) 3D Gaussian Splatting framework that progressively recovers both camera poses and 3DGS representation via local-to- global optimization paradigm. In the local stage, we introduce lightweight local neural geometric representation that is jointly optimized depth priors, thereby enabling accurate reconstruction in textureless
short video clips under supervision from multi-frame
tracking and foundation-mode regions and under abrupt camera motion. In the global stage, we fuse the resulting local 3D Gaussians into a unified 3DGS representation through alternating optimization, ensuring geometric consistency under challenging conditions. Extensive experiments on public datasets demonstrate that our method achieves state-of-the-art performance in both camera pose estimation and novel view synthesis, extending the applicability of 3DGS to real-world, unconstrained environments.
Despite the improved robustness in indoor scenarios and better handling of complex camera motion, several limitations remain. First, the local stage introduces additional computational overhead compared with vanilla 3DGS. Second, even with local-to-glon optimization and multiple loss constraints, the method can fail under extremely rapid camera motion or severely sparse inputs. We aim to address these issues in future work.
Societal Impact. This technology benefit AR/VR, robotics, digital content creation, telepresence, and cultural heritage preservation. However, its computational demands may contribute to higher carbon footprint.
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Acknowledgment. This study was supported in part by National Key R&D Program of China Project 2022ZD0161 100, in part by the Centre for Perceptual and Interactive Intelligence, CUHK-led InnoCentre under the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government, in part by NSFC-RGC Project N CUHK498/24, and in part by Guangdong Basic and Applied Basic Research Foundation (No. 2023B1515130005. XW).
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The abstract and introduction clearly describe two main contributions: (1) A local-to-glose -loobllllllllo optimization framework for pose-free 3DGS, and (2) A neural geometric representation that enhances robustness under textureless regions and abrupt motion. These are consistently supported by our experiments and analysis.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results
be expected to generalize to other settings. It is fine to include aspirational goals as motivation long it is clear that these goals are not attained by the paper.
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Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
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The paper did talk about the limitations of the work in the last section
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3. Theory assumptions and prool's
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
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Answer: [NA]
Justification: This work is primarily empirical, focus on the application, and does not present any formal theorems or theoretical analysis requiring assumptions proofs.
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Reviews:
[{"review_id": "2yaJybDdLx", "reviewer": "Reviewer_Hqtq", "summary": "This 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.", "questions": "1. 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", "limitations": "Yes. The paper mentioned the high computational demands may contribute to higher carbon emission.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**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", "was_revised": true, "final_justification": "Thank you to the authors for the detailed rebuttal. I believe the majority of questions have been clearly addressed.\n\nAt this point, I have no major remaining concerns with the paper. However, I would like to offer two comments to conclude my response:\n\n1. The authors clarified that only eight scenes from the Tanks and Temples dataset were evaluated, which is the same protocol being used in some of the previous baselines. This detail (which specific scenes) should be explicitly stated in the final paper to avoid potential confusion. Since the authors have promised to include full dataset results in the camera-ready version, I am leaning towards raising my rating.\n\n2. I also agree with Reviewer Ht6p that the term \"pose-free\" may feel somewhat strong or idealized, given the current state of the field. While not technically incorrect, it could give readers the impression that the problem is well solved, which may not reflect the reality. Alternative terms such as \"COLMAP-free\" or \"unknown-pose\" might be more precise and aligned with existing literature.\n\nOverall, I believe the strengths of this paper outweigh its weaknesses, and I am therefore glad to increase my rating to Borderline Accept (BA).", "rebuttal": "# **Reviewer Hqtq**\n\nDear Reviewer Hqtq,\n\nWe sincerely appreciate the valuable feedback. \n\nWe address each point below and will further elaborate during the rolling review, given the word limit.\n\n**Q-1: The paper’s claim of overcoming reliance on COLMAP poses is unconvincing. Evaluation is limited to datasets partially solvable by prior methods and omits the scenes in Tanks and Temples, where COLMAP excels. A broader evaluation is needed to support the pose-free claim.**\n\n**A-1:** Thank you for raising this concern. We would like to clarify the misunderstanding regarding our method’s pose-free claim and its evaluation scope.\n\n1. **Pose-Free Pipeline**\n    \n    Our method is indeed pose-free, which does not require external camera poses from COLMAP, SLAM, or IMU. Instead, it jointly optimizes camera poses and 3D Gaussians from monocular RGB inputs via a two-stage process: clip-wise optimization using our Local Neural Geometric Representation (LNGR), followed by global pose refinement and Gaussian merging.\n    \n2. **Evaluation on Tanks and Temples**\n    \n    Contrary to the concern, we do evaluate on the full Tanks and Temples dataset, following the standard protocol adopted by CF-3DGS, without cherry-picking. As shown in Table 3 of the manuscript and Table R1-1, our method consistently outperforms CF-3DGS, BARF, and NopeNeRF, even on scenes where COLMAP typically performs well.\n    \n3. **Robustness in Challenging Scenarios**\n    \n    As demonstrated in Tables 1–4 and Figure 1 of our manuscript, our method exhibits stronger robustness than prior works (CF-3DGS, NopeNeRF, BARF) across both indoor and outdoor datasets, including Replica, ScanNet, and Tanks and Temples, particularly in textureless scenes and under abrupt camera motion, where many existing methods struggle or fail.\n    \n\nWe will revise the manuscript to emphasize these points more clearly. Thank you again for highlighting this issue.\n\n*Table R1-1: Camera pose estimation and novel view synthesis performance metrics comparison on Tanks and Temples dataset(mean values).*\n\n| Methods | ATE ↓ | PSNR ↑ | SSIM ↑ | LPIPS ↓ |\n| --- | --- | --- | --- | --- |\n| COLMAP+3DGS | - | 30.20 | 0.92 | 0.10 |\n| BARF | 0.078 | 23.42 | 0.61 | 0.54 |\n| NopeNeRF | 0.006 | 26.34 | 0.74 | 0.39 |\n| NoPoSplat | 0.117 | 20.15 | 0.53 | 0.47 |\n| CF-3DGS | 0.004 | 31.28 | 0.93 | 0.09 |\n| Ours | 0.003 | 31.68 | 0.94 | 0.07 |\n\n**Q-2**:  **The scope is too narrow; The proposed local-to-global optimization seems to be very similar to GS-based SLAM methods. Articulate the difference.**\n\n**A-2:** We thank the reviewer for raising this valuable point. \n\nWe provide a detailed clarification below:\n\n1. **Scope and Generalization.**\n    \n    Our method, though designed for challenging indoor scenes, generalizes well to outdoor scenarios without tuning, as shown by strong results on the Tanks and Temples dataset (Sec. 4.4 in the manuscript), which contains outdoor scenes, demonstrating that our method generalizes well beyond indoor cases.\n    \n    Moreover, challenging indoor scenarios with textureless environments or abrupt camera motion (e.g., the blank walls or the abrupt movement of the AR kit), targeted by our method NopeRoomGS, are not rare. They are quite common in real-world applications, e.g., AR/VR and indoor assistive robotics.\n    \n2. **Distinctions from GS-based SLAM Methods.** \n    \n    (1). **The novelty of our Local-to-global optimization.** \n    \n    We acknowledge that the conventional GS-based SLAM methods separate the optimization into a tracking phase and a global optimization phase. Conceptually, our local stage corresponds to the tracking phase in the GS-based SLAM methods (e.g., SGS-SLAM). However, our local stage is fundamentally different from these methods. The conventional methods track the camera frame by frame, which is shown to be unreliable in our setting (Fig.6 in the manuscript). \n    \n    Our local stage uses LNGR to jointly process a set of adjacent frames in one batch, offering more robust optimization than frame-by-frame tracking, especially under low texture or abrupt motion.\n    \n    (2). **Different assumptions and application domains.**\n    \n    GS-based SLAM methods assume high frame rates and small inter-frame motion, relying on temporal continuity by initializing new poses from the previous frame.\n    \n    In contrast, pose-free GS reconstruction methods are tailored for offline novel view synthesis and can operate on sequential video input or unordered image collections.\n    \n    Our NopeRoomGS requires no temporal ordering for pose initialization and is built to handle abrupt motion and textureless regions where SLAM methods often fail (Table R1-2).\n    \n3. **Comparison with more GS-based SLAM methods.**\n    \n    To demonstrate the competitiveness of our method, we compare it with more recent GS-SLAM methods in Table 2 in our manuscript. \n    The method mentioned by the reviewer, *GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting, has not released its code to date*. And we substitute it with *MonoGS: Gaussian Splatting SLAM*. Following the same protocol and using RGB images as input, the results are reported below.\n    \n    *Table R1-2: Comparison of methods in terms of camera pose accuracy and rendering quality on the Replica dataset.*\n    \n    | Method | ATE ↓ | PSNR ↑ | SSIM ↑ | LPIPS ↓ |\n    | --- | --- | --- | --- | --- |\n    | GS3LAM | 0.043 | 24.35 | 0.69 | 0.36 |\n    | SGS-SLAM | 0.037 | 25.45 | 0.70 | 0.33 |\n    | MonoGS | 0.039 | 27.40 | 0.73 | 0.29 |\n    | Ours | 0.009 | 32.14 | 0.90 | 0.09 |\n\n**Q-3: Alternating optimization is not new—SfM methods like [4] also use it. Could the paper better explain why it is especially effective in your pose-free 3DGS setting?**\n\n**A-3**: Our alternating optimization is inspired by SfM methods like [4]. \n\nHowever, unlike [4], which optimizes sparse 3D points via geometric reprojection, our pose-free 3D Gaussian Splatting (3DGS) jointly optimizes a high-dimensional set of tightly coupled parameters, including geometry (position, scale, orientation) and appearance (color, opacity), that interact with poses through differentiable rasterization.  \n\nThis creates a complex, nonlinear photometric loss where errors stem from pose drift, shape misalignment, or appearance mismatch. Under this complex situation, the optimization of camera poses and rendering fidelity could induce gradient conflicts and compromise each other. \n\nThe proposed alternating optimization strategy in the global optimization stage can mitigate this issue, resulting in more robust pose estimation and superior rendering quality (See Fig. 6 and Table 5 in our manuscript). Due to the time limit during the rebuttal phase, we will include more analysis on alternating optimization in our final version.  \n\n**Q-4: Clarify the selected scene of ScanNet.**\n\n**A-4:** For scene selection, we follow the work, *Neural 3D Scene Reconstruction with the Manhattan-world Assumption* , and use the sequences: scene0616_00, scene0580_00, scene0084_00, and scene0050_00.  We will include these details in the final version and release the data.\n\n**Q-5:  The proposed local-to-global adds complexity, so provide runtime and memory.**\n\n**A-5:** As noted in Sec. 3.1, in the local stage, our LNGR module is lightweight (~30K trainable params), requiring only 2 GB memory,  2 minutes, and 2000 steps per 20-frame clip. For a 100-frame sequence (5 clips), LNGR adds ~10 minutes total overhead.\n\nOur full pipeline runs in <90 minutes per scene on an RTX 4090 with <12 GB peak memory. We will include this runtime analysis in the final version.\n\n*Table R1-4: Comparison of methods in terms of overhead*\n\n| Method | Time(min) | Memory(GB) |\n| --- | --- | --- |\n| NopeNeRF | ~400 | 6 |\n| CF-3DGS | ~63 | 9 |\n| Ours | ~81 | 12 |\n\n**Q-6:** **Comparison with  SFGS-HT, which seems to perform better in the Tanks dataset.**\n\n**A-6:** We thank the reviewer for highlighting this relevant work. We acknowledge that the concurrent work, SFGS-HT: *SfM-Free 3D Gaussian Splatting via Hierarchical Training*, performs better on the Tanks and Temples dataset, but may fail to converge on indoor scenes with severe motion and low-texture regions. We evaluate SFGS-HT on the indoor dataset Replica. Its performance degrades easily, as shown in Table R1-5, while our method shows robustness.  \n\n*Table R1-5: Comparison of methods in terms of camera pose metrics and rendering quality on the Replica dataset.*\n\n| Method | ATE ↓ | PSNR ↑ | SSIM ↑ | LPIPS ↓ |\n| --- | --- | --- | --- | --- |\n| SFGS-HT | 0.021 | 26.11 | 0.78 | 0.18 |\n| Ours | **0.009** | **32.14** | **0.90** | **0.09** |\n\n**Q-7: Minor Weaknesses: (1) LNGR is not a good name. How does the representation differ from depth? (2) Clarify the difference with pose-free methods, such as Splatt3R and InstantSplat.**\n\n**A-7:** Thank you for your comments. \n\n(1) LNGR builds on a pretrained CNN depth model to jointly predict depth over short clips. It is finetuned with multi-frame geometric consistency to correct scale drift from monocular depth estimation (see Sec. 3.1 of our paper). Conceptually, it learns a consistent local geometric representation. We will revise the module name for clarity.\n\n(2) Recent feed-forward methods like Splatt3r and InstantSplat directly output 3DGS representation, taking several input views,(e.g., 2 frames in Splatt3r, ~10 frames in InstantSplat) , recovering limited scene. These types of methods focus on the generalization, usually have difficulties in high-resolution rendering  (only output 512x512) due to their high memory consumption. In contrast, our method follows the track of a per-scene optimization pipeline (similar to SfM, NeRF, 3DGS), focusing on how to robustly optimize the representation for each scene separately."}, {"review_id": "WmoQgaYS9B", "reviewer": "Reviewer_Xtpw", "summary": "In 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.", "questions": "Please refer to the weakness part.", "limitations": "Yes.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "I have read them carefully, and they address all of my initial comments. Please incorporate these discussions into the final version for completeness. Therefore, I would like to maintain my original positive rating.", "rebuttal": "# Reviewer Xtpw\n\nDear Reviewer Xtpw,\n\nThank you for your thoughtful and professional feedback and highly valuable comments. We sincerely appreciate your recognition of the motivation and methodology underlying our work.\n\n**Q-1:** **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.**\n\n**A-1:** We appreciate the reviewer’s thoughtful observation regarding the potential scale-drift issue. \n\nIn our framework, each local clip shares at least one frame with its preceding clip, as illustrated in Fig. 2 in our paper. During global fusion, clips are merged sequentially. Suppose we have already fused the first $n$ frames, that is ${1, 2, ..., n}$ ; when merging the next local clip consisting of frames ${n, n+1, ..., n+l}$ (where $l$ is the clip length), frame $n$ serves as the overlapping frame.\n\nIn the local optimization stage, we fix the camera pose of the shared frame $n$ to its globally optimized pose from the previous stage. We then jointly optimize the poses and depths of the remaining frames ${n+1, ..., n+l}$, using scale-and-shift-invariant regularization loss, as described in equation(4) of Sec. 3.1. This design ensures that the new clip is aligned in scale with the previously fused global model. As a result, the metric scale is propagated from one clip to the next, maintaining a consistent global scale across the entire sequence.\n\nWe will incorporate this explanation into Sec. 3.2 and explicitly reference Fig. 2 to improve clarity.\n\n**Q-2:** **It would be better to have memory and time comparisons with previous methods**\n\n**A-2**: We thank the reviewer for the valuable suggestion. To address this point, we report detailed runtime and memory usage and compare with representative methods, including Nope-NeRF [1] and CF-3DGS [2].\n\nOur full pipeline takes ~81 minutes per scene on an RTX 4090 GPU, with peak memory usage under 12 GB. The local stage adds ~10 minutes overhead (due to LNGR), while the global stage takes ~71 minutes.\n\nAs shown in Table R2-1, under the same 100-frame setting, our method offers a favorable trade-off between runtime and reconstruction quality. It is slightly slower than CF-3DGS but achieves greater robustness in challenging indoor scenes, and is significantly more efficient and scalable than Nope-NeRF in both training and rendering.\n\nTable R2-1: Runtime and memory usage comparison on 100-frame sequences\n\n| Method | Time (min) | Memory(GB) |\n| --- | --- | --- |\n| NopeNeRF | ~400  | 6 |\n| CF-3DGS | ~63 | 9 |\n| Ours | ~81 | 12 |\n\nWe will incorporate this analysis into the revised manuscript to improve clarity and better contextualize the efficiency of our method.\n\nReference: \n\n[1]. NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior\n\n[2]. CF-3DGS: COLMAP-Free 3D Gaussian Splatting\n\n**Q-3: 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\n**A-3:** We sincerely thank the reviewer for this insightful question and the opportunity to clarify the conceptual and practical distinctions between 3DGS-based SLAM methods and pose-free 3DGS reconstruction methods. \n\nWhile both paradigms reconstruct 3D scenes and estimate camera poses from monocular RGB inputs using Gaussian Splatting, they differ fundamentally in their assumptions, goals, optimization strategies, and robustness.\n\n1. **Different Assumptions and Application Domains.**\n    \n    3DGS-based SLAM methods are designed primarily for online or real-time applications. They assume sequential input with small inter-frame motion and often leverage loop closure mechanisms to ensure long-term consistency. These methods often initialize the pose of the newly arrived frame using the previous frame's pose, heavily relying on temporal continuity.\n    \n    In contrast, pose-free 3DGS methods, including ours (NopeRoomGS), are mainly designed for offline novel view synthesis and can operate on sequential video input or unordered image collections. \n    \n    Our NopeRoomGS does not rely on temporal ordering for  pose initialization. It is specifically designed to handle challenging scenarios with abrupt camera motion and large textureless regions, where traditional SLAM-based methods often fail to initialize or maintain accuracy.\n    \n2. **Different Optimization Focus: Localization vs. Photorealism**\n    \n    The primary objective of SLAM-based 3DGS methods is **accurate camera localization** and **online mapping**. As such, their pipelines are optimized for **pose/map accuracy**, with rendering quality being a secondary concern.\n    \n    In contrast, pose-free 3DGS methods like ours focus on producing high-fidelity novel view synthesis alongside robust camera pose estimation. In our method, this is achieved through a carefully designed alternating optimization strategy in the global stage, which decouples the optimization of camera poses and 3D Gaussian parameters to mitigate gradient conflicts. This results in superior rendering fidelity and competitive, often superior, pose accuracy.\n    \n3. **Quantitative Comparison**\n    \n    To further clarify the differences, we compare our method with three representative GS-SLAM methods: GS3LAM [1], SGS-SLAM [2], and MonoGS [3], on the Replica dataset, following the same input protocol using only monocular RGB images.\n    \n    As shown in Table R2-2, our method significantly outperforms SLAM-based counterparts across all metrics, including absolute trajectory error (ATE) and photometric quality, highlighting the effectiveness of our local-to-global pipeline in challenging indoor environments.\n    \n\n*Table R2-2: Comparison of methods in terms of camera pose accuracy and rendering quality on the Replica dataset*\n\n| Method | ATE ↓ | PSNR ↑ | SSIM ↑ | LPIPS ↓ |\n| --- | --- | --- | --- | --- |\n| GS3LAM | 0.043 | 24.35 | 0.69 | 0.36 |\n| SGS-SLAM | 0.037 | 25.45 | 0.70 | 0.33 |\n| MonoGS | 0.039 | 27.40 | 0.73 | 0.29 |\n| **Ours** | **0.009** | **32.14** | **0.90** | **0.09** |\n\nWe will incorporate this detailed comparison and explanation into the revised manuscript to provide a clearer understanding of the distinctions and advantages of our method.\n\nReference: \n\n[1] GS3LAM: Gaussian Semantic Splatting SLAM, ACM MM.\n\n[2] SGS-SLAM: Semantic Gaussian Splatting for Neural Dense SLAM.\n\n[3] MonoGS: Gaussian Splatting SLAM with Monocular RGB Input."}, {"review_id": "V7GsBY4gem", "reviewer": "Reviewer_Ht6p", "summary": "Traditional 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.", "questions": "1. 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.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "This 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.", "was_revised": true, "final_justification": "My questions have been satisfactorily addressed. The paper is technically solid and contains a certain degree of innovation.", "rebuttal": "# Reviewer Ht6p\n\nDear Reviewer Ht6p,\n\nWe sincerely thank you for the detailed and thoughtful review. We appreciate the positive comments on the clarity, structure, and technical soundness of our pipeline. We address the raised concerns below.\n\n**Q-1: 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\n**A-1:** We sincerely thank the reviewer for highlighting this point.\n\nConsistent with previous work (e.g. CF-3DGS, NopeNeRF), we define pose-free as requiring no pre‑computed camera extrinsics from SfM, SLAM, or IMU. Our pipeline therefore begins with unknown poses and optimises them.\n\nWhile we do leverage pretrained models, CoTracker and RepDepth (also known as **Marigold**, as it is more commonly cited),  as weak priors during local stage, their outputs are treated as soft guidance, not ground truth, because the depth scale can differ from frame to frame. To obtain a consistent local scene structure, a lightweight Local Neural Geometric Representation (LNGR) is trained to align with, not copy, these noisy cues in the local stage; both depth and tracks are subsequently refined during the global optimization. As neither module supplies external 6‑DoF poses, the entire pipeline remains genuinely SfM‑free.\n\nWe hope this clarifies our use of pretrained components and why the method remains within the accepted scope of pose‑free reconstruction.\n\n**Q-2: 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\n**A-2:** We thank the reviewer for this constructive suggestion. \nTo evaluate the robustness of our method and the replaceability of different depth and correspondence predictors, we replace the pretrained models: “RepDepth” and CoTracker, with simpler or classical alternatives:  \n\n(1) The depth prediction model “RepDepth” ( ～1B parameters) is replaced with MiDaS ( ～20M parameters), a lightweight monocular estimation depth model;  \n\n(2) The motion prior CoTracker( ～44 M parameters) is replaced with the classical two-frame optical flow model RAFT ( ～5M parameters).\n\nWe evaluated these variants on the Replica dataset. As shown in Table R3-1,  although the performance could slightly degrade with these weaker models, the system still converges robustly and outperforms existing SfM-free baselines(CF-3DGS: 0.059(ATE), 25.40(PSNR), Table 1 and Table 4 of the manuscript). This suggests that our method can robustly work with different types of pretrained foundation models, demonstrating the effectiveness of our proposed modules.\n\n*Table R3-1: Comparison of methods with different model priors on the Replica dataset*\n\n| Method | ATE ↓ | PSNR ↑ | SSIM ↑ | LPIPS ↓ |\n| --- | --- | --- | --- | --- |\n| Ours (CoTracker + Marigold) | 0.009 | 32.14 | 0.90 | 0.09 |\n| Ours (CoTracker + Midas) | 0.018 | 30.23 | 0.90 | 0.09 |\n| Ours (RAFT + Marigold) | 0.020 | 29.14 | 0.89 | 0.10 |\n| Ours (RAFT + Midas) | 0.024 | 29.79 | 0.83 | 0.15 |\n\n**Q-3**: **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\n**A-3:** We thank the reviewer for the valuable suggestions regarding reproducibility and transparency. \n\n1. **Code and Reproducibility.** \n    \n    Due to rebuttal guidelines, we are unable to include any external links or non-anonymized scripts at this stage. As noted in the paper, we will publicly release the code and trained models upon acceptance to ensure full reproducibility. \n    \n2. **Resource Usage.** \n    \n    In response to the reviewer’s suggestion, we include a table summarizing the resource usage (e.g., memory and training time) and elaborate on the main computational bottlenecks in our pipeline below.\n    \n    As shown in Table R3-2, the total runtime of our pipeline is less than 90 minutes per scene on an RTX 4090 GPU, with peak memory usage below 12 GB. The additional time and memory overhead primarily stems from the Local Neural Geometric Representation (LNGR) and the inference of foundation models (RepDepth and CoTracker). While these components introduce modest computational overhead, they significantly improve reconstruction quality, as shown in Table 5 of the paper, where PSNR increases from 14.82 dB to 32.14 dB.\n    \n\n*Table R3-2: Comparison of methods in terms of overhead*\n\n| Method | Time (min) | Memory(GB) |\n| --- | --- | --- |\n| NopeNeRF | ~400 | 6 |\n| CF3DGS | ~63 | 9 |\n| Ours | ~81 | 12 |\n\n**Q-4: 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\n**A-4:** Thanks for your comment.\n\n1. **Theoretical Explanation**\n    \n    We appreciate the reviewer’s request for clarification regarding the robustness of our local-to-global strategy in low-texture and fast-motion scenarios.\n    \n    One of the key distinctions of our framework compared to prior pipelines (e.g., CF-3DGS) lies in how initial camera poses are optimized. Specifically, we segment the video into short, overlapping clips and perform joint pose and geometry optimization at the clip level, rather than frame-by-frame. This clip-wise formulation improves robustness to fast camera motion in two ways:\n    \n    **(1). Better-conditioned optimization.**\n    \n    Each clip forms a small-scale multi-view reconstruction problem with overlapping views, which improves numerical stability and optimization convergence.\n    \n    **(2).Stronger geometric constraints.**\n    \n    Multi-view consistency across multiple frames enables more accurate pose disambiguation than pairwise (frame-to-frame) approaches, particularly when motion blur or abrupt motion causes image misalignment. \n    \n    In low-texture scenarios, traditional geometric methods often struggle due to a lack of reliable visual features. Our method addresses this by integrating dense depth and correspondence priors from pretrained foundation models (RepDepth and CoTracker) during the local stage. These priors provide global structure-aware cues that are especially beneficial in regions where SIFT/SfM-based methods may fail. The priors act as soft constraints that guide the local optimization even in the absence of strong appearance gradients.\n    \n2. **Quantitative Comparison**\n    \n    To better validate the robustness of L2G structure,  we conducted an ablation study, increasing the difficulty of the reconstruction task by downsampling the input video frames at varying intervals (interval steps = 1, 5, 10).\n    \n    We compare our full method with a baseline that disables the L2G mechanism (“Ours w/o L2G”), which performs camera pose optimization similar to prior work. As shown in Table R3-3, our method with L2G structure consistently yields lower Absolute Trajectory Error (ATE) and higher rendering quality metrics. These results confirm that the L2G structure significantly improves robustness and accuracy under precisely the challenging conditions described.\n    \n\n*Table R3-3: Comparison of methods in terms of robust on Replica in different sample steps.*\n\n| Method | Step | ATE ↓ | PSNR ↑ | SSIM ↑ | LPIPS ↓ |\n| --- | --- | --- | --- | --- | --- |\n| Ours w/o L2G | 1 | 0.032 | 30.32 | 0.77 | 0.21 |\n| Ours w/o L2G | 5 | 0.052 | 24.40 | 0.73 | 0.30 |\n| Ours w/o L2G | 10 | 0.059 | 25.71 | 0.67 | 0.33 |\n| Ours w L2G | 1 | **0.007** | **34.16** | **0.91** | **0.09** |\n| Ours w L2G | 5 | **0.010** | **33.23** | **0.90** | **0.11** |\n| Ours w L2G | 10 | **0.009** | **32.14** | **0.90** | **0.09** |\n\nWe will incorporate this explanation and analysis into the revised manuscript to improve clarity and completeness."}, {"review_id": "90DAhLaGd5", "reviewer": "Reviewer_AuKd", "summary": "This 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.", "questions": "1. 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?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Streath:\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.", "was_revised": true, "final_justification": "Thank you to the authors for carefully considering my review comments and providing detailed responses. I believe the authors’ replies have basically addressed my concerns. It was my oversight not to provide references [1] and [2], but since the authors have supplemented comparison experiments with MonoGS and DROID-SLAM and demonstrated the advantages of the current method, I consider this part of my doubts to be resolved. The authors’ explanation regarding the motivation of LNGR is convincing to me, and the experiments also validate the rationality of the LNGR optimization representation. I think this aspect is both innovative and insightful, and the authors should include the corresponding ablation studies and analysis in the main text. In summary, I consider this paper technically solid and possessing a certain degree of novelty, so I recommend (weakly) accepting it.\n\n[1]Matsuki H, Murai R, Kelly P H J, et al. Gaussian splatting slam[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 18039-18048. [2] Zhang W, Cheng Q, Skuddis D, et al. HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction[J]. arXiv preprint arXiv:2411.17982, 2024.", "rebuttal": "# Reviewer AuKd\n\nDear Reviewer AuKd,\n\nWe thank you for acknowledging our method’s effectiveness in tackling key challenges in pose-free 3DGS, particularly under textureless scenes and abrupt camera motion. We address your concerns below.\n\n**Q-1**: **The paper lacks discussion and comparison with monocular Gaussian SLAM methods. Existing approaches [1][2] also recover camera poses and 3D Gaussians from RGB input. Based on reported results, the proposed method does not clearly outperform monocular Gaussian SLAM [2] in rendering quality or pose accuracy.**\n\n**A-1:** We sincerely thank the reviewer for raising this important point.\n\n**We notice that the specific literatures you refer to with [1] [2] are not provided in the review.**\n\nWe assume they are works similar to the recent monocular Gaussian SLAM methods such as GS3LAM [a] and MonoGS [b]. As far as we know, most of the Gaussian SLAM are RGB-D based, which assume accurate depth inputs for camera pose estimation. However, considering the depth measurements are usually not available in daily settings, **our method only takes RGB as input which is more challenging**.  The pose-free GS and Gaussian SLAM are two related but different tracks of research. In the following, we will elaborate on the difference between them and add more comparisons with the Gaussian SLAM works.\n\n1. **Distinction from Gaussian SLAM Methods.**\n    \n    While both paradigms reconstruct 3D scenes and estimate camera poses from monocular RGB inputs using Gaussian Splatting, they differ fundamentally in their assumptions, application domains, and optimization strategies.\n    \n    **(1). Different assumptions and application domains.**\n    \n    3DGS-based SLAM methods are designed primarily for online or real-time applications. They assume sequential input with small inter-frame motion and often leverage loop closure mechanisms to ensure long-term consistency. These methods often initialize the pose of the newly arrived frame using the previous frame's pose, heavily relying on temporal continuity.\n    \n    In contrast, pose-free 3DGS methods, including ours (NopeRoomGS), are mainly designed for offline novel view synthesis and can operate on sequential video input or unordered image collections.\n    \n    Our NopeRoomGS does not rely on temporal ordering for pose initialization. It is specifically designed to handle challenging scenarios with abrupt camera motion or textureless regions, where traditional SLAM-based methods often fail to initialize or maintain accuracy.\n    \n    **(2). Different application scenarios: localization vs. photorealistic rendering.**\n    \n    The primary objective of SLAM-based 3DGS methods is accurate camera localization and online mapping. As such, their pipelines are optimized to pursue accurate pose/map estimation, with rendering quality being a secondary concern.\n    \n    In contrast, the pose-free 3DGS methods like ours focus on producing high-fidelity novel view synthesis alongside robust camera pose estimation.\n    \n    In practice, the optimization of camera poses and rendering fidelity could induce gradient conflicts and compromise each other. In our method, we found that the proposed alternating optimization strategy in the global optimization stage can mitigate this issue, resulting in more robust pose estimation and superior rendering quality.\n    \n2. **Additional Comparison with Gaussian SLAMs**\n    \n    As you forgot to explicitly mention the method you want us to compare with, we select three recent competitive monocular SLAM baselines: GS3LAM [a], MonoGS[b], and SGS-SLAM [c], and compare our method with them on the Replica dataset using monocular RGB input for fairness. As shown in Table R4-1, our method outperforms other methods in all metrics for rendering quality and pose accuracy, demonstrating the strength of our optimization design.\n    \n\n*Table R4-1: Comparison of methods in terms of camera pose accuracy and rendering quality on the Replica dataset*\n\n| Method | ATE ↓ | PSNR ↑ | SSIM ↑ | LPIPS ↓ |\n| --- | --- | --- | --- | --- |\n| GS3LAM | 0.043 | 24.35 | 0.69 | 0.36 |\n| SGS-SLAM | 0.037 | 25.45 | 0.70 | 0.33 |\n| MonoGS | 0.039 | 27.40 | 0.73 | 0.29 |\n| Ours | **0.009** | **32.14** | **0.90** | **0.09** |\n\nWe appreciate the reviewer’s suggestion and will include this discussion and comparative results in the revised manuscript to better contextualize our contributions.\n\nRef:\n\n[a]. GS3LAM: Gaussian Semantic Splatting SLAM.\n\n[b]. MonoGS: Gaussian Splatting SLAM.\n\n[c]. SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM\n\n**Q-2:** **Further explain the motivation for using a neural depth network. Moreover, a comparison with per-pixel optimizable depth (initialized from the same network) is needed to justify this design choice.**\n\n**A-2**:  We sincerely thank the reviewer for the insightful question. Below, we elaborate on the motivation for our Local Neural Geometric Representation (LNGR) and discuss the advantages over a per-pixel optimizable depth design.\n\n1. **Motivation of LNGR**\n    \n    **(1) Ensuring Geometric Consistency Across Frames.**\n    \n    LNGR provides a shared depth representation across all frames in a clip, which encourages multi-view geometric consistency and helps reduce scale drift (a common issue in frame-wise optimization pipelines like CF-3DGS). This global structure is difficult to enforce when depths are independently optimized per frame.\n    \n    **(2) Improving Optimization Stability and Efficiency.**\n    \n    Directly optimizing dense per-frame depth maps introduces millions of parameters (e.g., >6M variables for 20 frames at 640×480 resolution), which tends to pose Jacobian deficiency, especially in textureless regions where valid visual constraints are limited.\n    \n    In contrast, our LNGR leverages strong depth priors from a pretrained foundation model and only fine-tunes the final prediction layers (~30K trainable parameters), improving robustness while avoiding overfitting, especially in ill-conditioned regions (e.g., flat walls or low-texture areas) where visual constraints are sparse.\n    \n2. **Ablation: LNGR vs. Per-Pixel Depth Optimization**\n    \n    To validate the effectiveness of LNGR, we implemented a baseline variant that directly optimizes per-pixel depth maps initialized from the same pretrained depth network. We refer to this baseline as “Ours (Pixel-wise Depth Optimization)”.\n    \n    As shown in Table R4-2, this baseline consistently yields larger pose errors and lower novel view synthesis quality. These results empirically validate the advantages of our LNGR design. We will include the full ablation results in the final version of the paper.\n    \n\n*Table R4-2: Comparison of methods in terms of camera pose metrics and rendering quality*\n\n| Method | ATE ↓ | PSNR ↑ | SSIM ↑ | LPIPS ↓ |\n| --- | --- | --- | --- | --- |\n| Ours (Pixel-wise Depth Optimization) | 0.049 | 28.46 | 0.82 | 0.34 |\n| Ours (LNGR) | 0.009 | 32.14 | 0.90 | 0.09 |\n\n**Q-3:** **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\n**A-3**:  Thank the reviewer for this valuable suggestion.\n\nTo address this point, we provide a detailed comparison of runtime and memory usage with representative prior methods, including Nope-NeRF  and CF-3DGS .\n\nOur full local-to-global optimization pipeline takes ~81 minutes for each scene running on an RTX 4090 GPU, with peak memory usage <12 GB. The local stage brings ~10 minutes overhead while the global stage takes ~71 minutes.\n\nWe provide a direct comparison with Nope-NeRF and CF-3DGS under the same 100-frame sequence setting. As shown in the Table R4-3, our method strikes a favorable balance between runtime and reconstruction quality. While slightly slower than CF-3DGS, our approach offers improved robustness in challenging indoor scenes and is significantly more efficient than Nope-NeRF in terms of both training/rendering efficiency and scalability.\n\n*Table R4-3: Comparison of methods in terms of overhead*\n\n| Method | Time(min) | Memory(GB) |\n| --- | --- | --- |\n| NopeNeRF | ~400 | 6 |\n| CF3DGS | ~63 | 9 |\n| Ours | ~81 | 12 |\n\n**Q-4: 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?**\n\n**A-4**: We thank the reviewer for the valuable suggestion.\n\nWhile our method and DROID-SLAM, VGG-SfM, and VGGT all aim to estimate camera poses, they differ fundamentally in formulation and assumptions. However, we fully agree that comparing with these methods, especially in pose accuracy, will strengthen our work.\nWe incorporated comparisons with VGG-SfM, VGGT, and DROID-SLAM on Replica dataset. As shown in Table R4-4, our method achieves consistently higher pose accuracy, further validating the advantages of our design.\n\n*Table R4-4:  Comparison of methods in terms of camera pose metrics on Replica dataset*\n\n| Method | RPE_t ↓ | RPE_r ↓ | ATE ↓ |\n| --- | --- | --- | --- |\n| VGG-SfM | 0.547 | 2.003 | 0.024 |\n| VGG-T | 0.521 | 4.547 | 0.013 |\n| DROID-SLAM | 0.783 | 5.234 | 0.054 |\n| Ours | 0.148 | 0.126 | 0.009 |\n\n**Q-5: Did the authors observe any failure cases during the experiments?**\n\n**A-5**: While our method demonstrates strong performance across diverse indoor and outdoor scenes (as shown in Table 1–4 of the paper), our system may fail under extremely challenging conditions. Specifically, as mentioned in the conclusion (Line 376–380 in the manuscript), our method may struggle in scenarios with extremely fast camera motion or overly sparse inputs, where both geometric consistency and photometric supervision become unreliable. We will include additional qualitative examples of such failure cases in the final version to provide a more comprehensive picture. Improving robustness under such challenging conditions is an important direction for future work."}]

Author rebuttal:

Meta-review: This paper introduced Pose-Free Indoor 3D Gaussian Splatting technique. Initially, the reviewers raised concerns regarding comparisons to GS-based SLAM methods, memory and time comparisons with previous methods, and comparisons with methods such as VGG-SfM, VGG-T, or DROID-SLAM. However, after the rebuttal, the authors successfully addressed most of these concerns, including the clarification of novelty. The AC has also reviewed the paper, reviewer comments, and rebuttal, agreeing that the paper is well-motivated, clearly written, and supported by thorough experiments. Therefore, the AC recommends acceptance. It would be beneficial to incorporate all additional experiments and discussions from the rebuttal into the final version.
Decision: Accept (poster)