Title: Geometrically Consistent Generalizable Splatting
Venue: NeurIPS 2025
Accepted: no
Keywords: Generalizable Gaussian Splatting

Geometry-Consistent Reconstruction

Self-Supervised Learning

Relative Pose Estimation

Novel View Synthesis
Abstract: Gaussian splatting has emerged as the preferred 3D scene representation due to its incredible speed and accuracy in novel view generation. Various attempts have thus been made to adapt multi-view structure prediction networks to directly predict per-pixel 3D Gaussians from images. However, most work has focused on enhancing self-supervised depth prediction networks to estimate additional parameters for 3D Gaussians -- orientation, scale, opacity, and appearance. We show that optimizing a view-synthesis loss alone is insufficient to recover geometrically meaningful splats in this simple manner. We systematically analyse and address the inherent ambiguities in learning 3D Gaussian splats with self-supervision to learn pose-free generalisable splitting. Our approach achieves state-of-the-art performance in 
(i) geometrically consistent reconstructions, 
(ii) relative pose estimation between images, and 
(iii) novel-view synthesis 
on the RealEstate10K and ACID datasets. We also showcase zero-shot capabilities of the proposed generalizable splatting on ScanNet, where our method substantially outperforms the prior art in recovering geometry and estimating relative pose.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this work, the authors first analyze the inherent ambiguities in the feed-forward Gaussian Splatting problem. They convert 3DGS to 2DGS and then introduce Align Loss and Orient Loss to enhance the consistency between Gaussian primitives. Experimental results show that their method achieves improved geometric consistency in reconstruction, more accurate relative pose estimation, and better novel view synthesis performance.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\n\n1. The analysis of the current feed-forward Gaussian Splatting problem is thorough and impressive.\n2. The authors introduce *Align Loss* and *Orient Loss* to enhance the consistency between Gaussian primitives, which appear to be highly effective.\n3. The experiments are comprehensive and strongly support the authors’ claims.\n\nCons:\n\n1. I believe this paper lacks novelty, despite achieving state-of-the-art performance in geometric reconstruction and relative pose estimation. The *Orient Loss* is a common strategy in SDF-based methods, similar to the curvature loss used in Neuralangelo. The *Align Loss* essentially functions as a form of bundle adjustment that optimizes the reprojection loss, leading to improved pose estimation. However, I am concerned that the *Align Loss* may significantly slow down training and may not scale well to multi-view settings. Additionally, are the camera extrinsics and intrinsics used in the *Align Loss* also solved via PnP?\n2. I find the pose estimation comparison with DUSt3R, MASt3R, and RoMa to be unfair. Since RE10K is not part of their training datasets, their results represent zero-shot performance, which makes the comparison less meaningful.\n3. I would like to know the training time for this method. In the supplementary material, the authors mention using 24 NVIDIA A100 GPUs, whereas NoPoSplat achieves training in 6 hours with only 8 GPUs.\n4. The novel view synthesis results shown in Table 4 of the supplementary material seem unusual. Why does 2DGS outperform 3DGS? Additionally, the *Orient Loss* appears to have little impact on novel view synthesis quality. Why does the combination of *Align Loss* and *Orient Loss* result in better rendering quality? I also suggest including qualitative comparisons in the novel view synthesis setting. Furthermore, what is the NVS performance of DepthSplat?\n5. I believe the paper should also include comparisons with FLARE, which is a closely related work.\n6. The coordinate conventions for normals in MVSplat and NoPoSplat appear to differ from those used in this work. This should be clarified.\n7. No demo was submitted.\n\nQUESTIONS:\n1. Could the authors provide the 3DGS ply files and rendering results instead of only showing mesh reconstructions? Because 2DGS performs better than 3DGS in terms of surface reconstruction quality.\n2. Why do the rendered depth maps appear less sharp compared to those from NoPoSplat and DepthSplat?\n3. I think pixel-wise Gaussian primitives may be overly redundant, and introducing more views significantly increases GPU memory usage. However, in this work, pixel-wise Gaussians seem crucial, likely because the method relies on PnP for pose estimation.\n\nLIMITATIONS:\nPlease see the weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-04"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a structurally constrained 2D Gaussian Splatting approach to improve orientation stability and geometric consistency in pose-free settings. It replaces the rank-3 covariance in NoPoSplat with a rank-2 version and adds two geometric regularizers: a pixel alignment loss and a directional consistency loss. The method outperforms existing approaches on RE10K, especially in normal consistency and pose estimation tasks.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper focuses on structure modeling in pose-free and generalizable settings, with a clear problem setup. By reducing the Gaussian covariance to rank-2 and introducing two geometric losses (L_align and L_orient), the method achieves solid performance on tasks like structure reconstruction, PnP pose estimation, and normal fusion. Training is stable, and the visual analysis in the appendix adds some credibility.\n\nHowever, the method itself is a relatively minor modification of NoPoSplat, with limited novelty. The rank reduction improves stability but also sacrifices expressiveness, making the model unsuitable for volumetric structures or multi-normal regions. More importantly, the paper doesn’t justify why these geometric losses can’t be applied directly to full-rank 3DGS. If they can, the rank-2 design becomes unnecessary. In addition, there’s no discussion of memory usage, efficiency, or applicability limits, leaving important questions unanswered.\n\nQUESTIONS:\n1. The authors should clarify why geometric losses like L_orient and L_align cannot be applied directly to full-rank 3DGS. If these losses are equally effective and stable in the original representation, then rank reduction is unnecessary and the design motivation becomes unconvincing.\n2. Does rank reduction lead to a loss of expressiveness—for example, the inability to model volume, thickness, or multi-normal regions? Has the impact of this on downstream tasks (e.g., relighting or reconstruction in fuzzy areas) been evaluated? The paper does not address this and should include relevant discussion.\n3. The paper claims improved training stability with rank reduction but provides no supporting efficiency data, such as memory usage, parameter count, or convergence speed. Without such evidence, the rank-2 design appears more like a trade-off than an actual advantage.\n4. Has the idea of using a mixed-rank strategy been considered? For instance, applying rank-2 in structurally clear regions and retaining rank-3 where uncertainty is high.\n\nLIMITATIONS:\nIt is recommended to explicitly include a “Limitations” section.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-20"}

comments: SUMMARY:
In this paper, the authors stated that a view-synthesis loss is not enough. To address this, after analysis, the authors add two additional regularization losses: orientation loss and alignment loss.

STRENGTHS AND WEAKNESSES:
**Strengths**
1. The authors analyze the veracity and geometric meaning of the Gaussian orientations and elongations.
2. The authors conducted extensive experiments to support their statements.

**Weaknesses**

1.This paper is somehow lacking novelty and contributions. The core parts are those two additional regularization terms, however, regarding the first loss, it shares similar high-level ideas with the ones in 2DGS, but with some technical adjustments. My questions on the second loss are in the following cells.

2. Typos in Line 210.

3. The result improvement is somehow tiny. In Figure 3 and 4, I understand that the depth should not be affected by the texture, but it seems the results from the proposed method is blurrier around the border regions.

I will consider increasing my ratings if the authors can answer my questions reasonably.

QUESTIONS:
1. In Line 9, the authors claimed their goal is to learn pose-free generalizable splatting, which means no pose provided. But in Line 221, the authors said, 'with known camera extrinsics'. This makes me confused.
2. Following question 1, if there is no GT pose involved during training, that is fine. If the 'known poses' are actually used during training, I would like to see the comparisons with other feed-forward reconstruction models using Gaussian Splatting supervised by GT poses during training like Splatt3r.

I will consider increasing my ratings if the authors can answer my questions reasonably.

LIMITATIONS:
Please refer to weaknesses.
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-06-25

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper tackles the task of “generalizable” gaussian splatting i.e. learning a feedforward predictor that, given a set of unposed images, infers a corresponding gaussian splatting representation. It largely builds on a prior framework (NoPoSplat) for this, but introduces additional regularizations to improve the quality of the inferred geometry: a) modifying 3D gaussians to 2D gaussians, b) encouraging the gaussians predicted by adjacent pixels to have similar orientations, c) enforcing the pixel-wise gaussians to lie along the corresponding ray.\n\nThe system is trained on RealEstate10k and evaluated on a couple real world datasets, and it demonstrates some improvements over the base model (NoPoSplat) in terms of the depth, pose, and view synthesis accuracy.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n+ This paper is well-motivated. The typical pipeline in training ‘generalizable’ gaussian splatting only optimizes for view synthesis loss, leading to under-constrained geometry. The goal in this paper is to overcome this limitation.\n\n+ The ideas presented in the paper are all sensible and well-implemented. The three regularizations added (2D gaussians, local normal consistency, and ray alignment) are all intuitive.\n\n+ The impact of the changes made is ablated well and the experiments show the benefits of adding one regularization component at a time.\n\nWeaknesses:\n\n- The contributions of this work are fundamentally limited to including regularizations in an existing framework. Even more so, regularizations such as these are also common (e.g. local normal consistency is a common term for mesh prediction, encouraging Gaussian to be like surfels is another common idea in recent work) — the contribution here is to merely apply these to the NoPoSplat framework.\n\n- While the paper shows consistent improvements over NoPoSplat, these are relatively marginal e.g. improved 2% accuracy in camera estimation or depth prediction (and similarly minor gains in pose). While this is not an issue on its own e.g. a paper showing a fundamentally new approach that is comparable to or marginally improves SoTA is ok, combined with the limited technical contribution, the marginal empirical gains make the impact of this work rather limited. Basically, the approach here is not very novel and the results are not very significant — and this combination limits the impact of the work.\n\n- The results are only shown on indoor scene-level datasets which predominantly have flat surfaces, but some of the regularizations e.g. local normal consistency maybe harmful in other domains e.g. object-centric datasets with high curvature in geometry. As such experiments on datasets like Co3D or Objaverse would have been very helpful in addition to reassure the reader about the contributions being broadly applicable.\n\nQUESTIONS:\nCould the authors provide some intuition on why there is a large improvement in Table 1 over NoPoSplat in the \"PnP + RANSAC” but this larger gains reduces after the photometric optimization?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-28"}