Title: Eve3D: Elevating Vision Models for Enhanced 3D Surface Reconstruction via Gaussian Splatting
Venue: NeurIPS 2025
Accepted: yes
Keywords: surface reconstruction

3d gaussian splatting
Abstract: We present Eve3D, a novel framework for dense 3D reconstruction based on 3D
Gaussian Splatting (3DGS). While most existing methods rely on imperfect priors
derived from pre-trained vision models, Eve3D fully leverages these priors by
jointly optimizing both them and the 3DGS backbone. This joint optimization
creates a mutually reinforcing cycle: the priors enhance the quality of 3DGS, which
in turn refines the priors, further improving the reconstruction. Additionally, Eve3D
introduces a novel optimization step based on bundle adjustment, overcoming the
limitations of the highly local supervision in standard 3DGS pipelines. Eve3D
achieves state-of-the-art results in surface reconstruction and novel view synthesis
on the Tanks & Temples, DTU, and Mip-NeRF360 datasets. while retaining fast
convergence, highlighting an unprecedented trade-off between accuracy and speed.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nMethod: \n1) run stereo matching on pairs of images with existing network [37]\n2) include depth priors into 3dgs reconstruction and run joint optimization / bundle adjustment to obtain 3dgs scene representation\n\nCore contribution is the use of a specific stereo matching network for better 3dgs reconstructions.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- compelling results and comparisons against the baselines in Table 1 and 2.\n- combining existing priors for surface reconstruction and 3DGS is always interesting.\n\nWeakness:\n- in the video, only a single scene is shown. The rest are mostly single objects which is a bit boring; the video rendering quality could be also better.\n- algorithmically, the method is straightforward and it seems the majority from the improvements come from better engineering; e.g., which depth estimation prior to use etc.\n- would be nice to see more ablations there how much that matters (also see below in the question section)\n\nQUESTIONS:\n- Is the stereo matching network trained on the respective datasets? How do you make sure there is no overlap in the train/test split?\n- How much do the priors help vs how much the joint optimization? I.e., I would like to see another entry in Table 1 and 2.\n- What if the baselines such as 2DGS would have the prior as a helper? I understand that there is some engineering to be done there as we well which this method contributes but at the same time the loss formulation is not that complicated either.\n- How would the surface look like if just the respective stereo depth maps were fused into a joint 3D scene representation (i.e., no 3dgs at all)? E.g., using something like a SDF-based reconstruction framework such as KinectFusion variants or Poisson Surface Reconstruction.\n\nI'm generally on the fence so I would like to see how the authors respond to the questions above.\n\nLIMITATIONS:\nyes, there is a limitation section.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a 3DGS framework for surface reconstruction with joint optimization of depth priors. Specifically, a virtual camera is utilized to perform a standard binocular stereo matching in a multi-view setting. Besides, a prior-involved local bundle adjustment is proposed to further eliminate the noise in depth prediction.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- The paper introduces a novel approach that jointly optimizes pre-trained vision model priors and the 3D Gaussian Splatting (3DGS) backbone. This creates a mutually reinforcing cycle where priors enhance 3DGS quality, which in turn refines the priors, leading to significantly improved 3D reconstruction accuracy. The framework addresses the limitations of existing methods that rely on static priors.\n\n- By introducing a local bundle adjustment strategy, the method overcomes the highly local supervision limitations of standard 3DGS pipelines. This approach enforces global consistency across co-visible frames during optimization, ensuring more accurate surface reconstruction and addressing challenges like textureless regions and repetitive patterns. The quantitative results on benchmarks like Tanks & Temples and DTU validate its effectiveness.\n\n- Eve3D achieves superior results in surface reconstruction and novel view synthesis while maintaining fast convergence. \n \nWeaknesses\n \n- The framework relies on a vision foundation model for stereo depth estimation, which introduces an overhead of rendering stereo images from the model. While this improves accuracy, it limits the method’s independence and could be problematic in scenarios where such models are not available or efficient. The paper acknowledges this as a limitation but does not provide a robust alternative.\n\n- Although the paper mentions that model-generated priors contain noise, the analysis of how different levels of prior noise affect reconstruction quality is insufficient. A more detailed ablation study on the impact of prior accuracy across various scenes would strengthen the understanding of the method’s robustness in noisy conditions.\n\n- The experiments primarily focus on standard benchmarks, but the method’s performance in extreme scenarios (e.g., highly dynamic environments, low-light conditions, or extremely large-scale scenes) remains unaddressed. The paper does not provide evidence of its effectiveness beyond the tested datasets, limiting confidence in its broader applicability.\n\nQUESTIONS:\n- How would the virtual camera affect the final results? Are the final results sensitive to the different choice of virtual cameras? An ablation study of different virtual cameras should be conducted. \n\n- The generalizability of this method should be well studied, as depth estimation may fail to predict the right depth scale. Are the results sensitive to the initialization of depth estimation?\n\n- Why did you choose depth priors other than normal or normal & depth?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose Eve3D, a 3D reconstruction method which jointly optimizes 3DGS and the depth priors supervised by 3DGS. It renders stereo pairs with a pretrained 3DGS and acquires initial depth maps from FoundationStereo. The depth maps are subsequently refined with local bundle adjustment in high-quality regions and guided by 3DGS depth in low-quality regions to improve multi-view consistency, which then serve as depth and normal supervision for 3DGS. The authors test Eve3D on DTU, Tanks and Temples, and Mip-NeRF 360 datasets, where it outperforms its counterparts in terms of reconstruction quality on most of the scenes. Substantial ablations are performed to validate the effectiveness of joint optimization and local bundle adjustment.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The idea of optimizing high-quality depth priors jointly with 3DGS to handle multi-view inconsistency and compensate for inaccuracies in the priors is straightforward but solid. The reported reconstruction performance on DTU and Tanks & Temples is also impressive, where the method achieves very competitive results even in equal-time comparisons (the Eve3D-fast variant).\n- Comprehensive ablations are conducted to study the effect of different depth priors, local bundle adjustment, and joint optimization. The results, in general, demonstrate the benefits of refining depth priors with local bundle adjustment and optimizing jointly with 3DGS, compared to previous works.\n\nWeaknesses:\n\nI do not observe any major weakness. Some minor issues include:\n- As Eve3D’s performance depends on the quality of initial depth priors to some degree (as discussed in supplementary material), which further depend on the stereo pairs rendered by pretrained 3DGS, it would be helpful to show and discuss how robustly Eve3D performs with sparse input views. In cases where the rendered stereo pairs are of low quality, it might be interesting to compare with priors from monocular depth estimation (or feeding identical views to the stereo models).\n- Pretraining 3DGS to render the stereo pairs and attaining the depth maps from FoundationStereo incur extra (though potentially minor) overhead on the pipeline. Currently there is little information on the time taken in this stage, and whether it is included in the reported Eve3D training time is unclear.\n- Some clarity-related issues, please refer to the questions part.\n\nQUESTIONS:\n1. Is the local bundle adjustment applied to the learnable depth priors only, or to 3DGS depth as well? Section 3.2 only mentions the depth priors, while supp. material mentions that both are optimized (L92-93).\n2. What are the specific settings for the ablations in Table 4, middle group (without the single-view prior loss)? Is the local bundle adjustment in this case only applied to 3DGS? Why would joint optimization make a difference here if the depth priors are unused in supervision?\n3. As discussed above, please clarify whether the training time reported takes pretraining and depth prior computation into account. Adding also a breakdown of preprocessing time (3DGS pretraining, stereo pairs rendering, FoundationStereo prediction, triangulation) would be more helpful.\n4. If possible, please add some discussions on the robustness of Eve3D in sparse-view settings (or when pretrained 3DGS/depth priors have poor quality), including visualizations and failure cases, if any.\n5. What is the number of neighboring views used in local bundle adjustment for the main results reported?\n6. For Fig. 8 in supp. material: it would be more informative to present a metrics vs. time plot with more data points till convergence, given that the number of neighbors used affects the training speed. Better if results of PGSR using more than one neighbor view per iteration are also presented.\n7. Is the confidence mask (L183) used solely in $\\mathcal{L}_{pull}$ (Eq. 12)? L280-284 hints that $\\hat{\\boldsymbol{M}}^c$ controls prior supervision regions, but this is not reflected in Eq. 13 and Eq. 14. Also, what is $\\Gamma$ in Eq. 14?\n8. L214, 216: Should be $T_{joint}$ instead of $T_{switch}$?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes Eve3D, a dense 3D reconstruction approach based on 3D Gaussian Splatting. Given that the depth priors obtained from pre-trained vision models are imperfect, a joint optimization mechanism is proposed in Eve3D, where the priors first improve the quality of 3D GS, which in turn refine the priors. This iterative process gradually improves the 3D reconstruction quality. Moreover, a bundle adjustment based optimization step is also introduced to overcome the local supervision of 3DGS. Experimental results on T&T, DTU and Mip-NeRF360 datasets demonstrate the effectiveness of the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. Extensive experiments have been conducted on three benchmarks, and the proposed method achieves better performance compared to existing works.\n2. Detailed ablation studies have been provided as well as the corresponding analysis.\n\nWeaknesses\n1. The presentation of the method is not very clear. There are different stages involved in the whole pipeline, which includes optimizing over 3D Gaussians and the learnable depth. However, it is not clear which losses are used at different stages, does the loss function in Eq.(10) only responsible for refining the learnable depth prior or both? After switching to optimizing the learnable depth prior, will it still switch back to optimize 3DGS? The optimizing targets are different before and after T_joint  to the text, but is not expressed in Eq (13)? In addition, Figure 2 is not clear about the alternative optimization.\n2. The proposed method involves complicate training process with many loss terms. Are the weights for different losses set to one according to Eq.(13)?  Are these weights values applicable to different datasets or hyperparameter tuning is required? How to decide the T_joint and how does it affect?\n3. The introduction of depth-normal consistency loss and the scale loss should be self-contained by either including references or descriptions.\n4. Missing references.\nVCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction. Neurips 2024.\n\nQUESTIONS:\nPlease refer to the weaknesses.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}