Title: VA-GS: Enhancing the Geometric Representation of Gaussian Splatting via View Alignment
Venue: NeurIPS 2025
Accepted: yes
Keywords: Gaussian Splatting

Neural Rendering

Surface Reconstruction

Novel View Synthesis
Abstract: 3D Gaussian Splatting has recently emerged as an efficient solution for high-quality and real-time novel view synthesis. However, its capability for accurate surface reconstruction remains underexplored. Due to the discrete and unstructured nature of Gaussians, supervision based solely on image rendering loss often leads to inaccurate geometry and inconsistent multi-view alignment. In this work, we propose a novel method that enhances the geometric representation of 3D Gaussians through view alignment (VA). Specifically, we incorporate edge-aware image cues into the rendering loss to improve surface boundary delineation. To enforce geometric consistency across views, we introduce a visibility-aware photometric alignment loss that models occlusions and encourages accurate spatial relationships among Gaussians. To further mitigate ambiguities caused by lighting variations, we incorporate normal-based constraints to refine the spatial orientation of Gaussians and improve local surface estimation. Additionally, we leverage deep image feature embeddings to enforce cross-view consistency, enhancing the robustness of the learned geometry under varying viewpoints and illumination. Extensive experiments on standard benchmarks demonstrate that our method achieves state-of-the-art performance in both surface reconstruction and novel view synthesis. The source code is available at https://github.com/LeoQLi/VA-GS.


Peer reviews:
comments: SUMMARY:
This paper proposes a new method for 3DGS-based surface reconstruction. It enforces multi-view color and feature consistency, as well as image gradient-guided local normal consistency, normal smoothness. The results show that the proposed method can achieve better performance on both reconstruction and rendering.

STRENGTHS AND WEAKNESSES:
Strengths:
1. The novel contribution in this paper is mainly the multi-view feature-level consistency, which learns from the MVS pipeline to align the high-dimensional features.

Weaknesses:
1. Significance: The results show no significant performance improvement over the previous state-of-the-art PGSR. According to Table 4, the proposed feature alignment only improves F1 Score by 0.01, and without which the performance becomes similar to PGSR. Therefore, I struggle to find the real significance of this work.

QUESTIONS:
1. What's the difference between the proposed Multi-View Photometric Alignment and the one in PGSR? An ablation study to illustrate the difference would be helpful.
2. In Equation 4, an ablation study to evaluate the effectiveness of the edge-aware coefficient is needed, since it's the main difference from the existing methods.

LIMITATIONS:
Please see the weaknesses and questions above.
TITLE: Official Review
CLARITY: 2
IMPACT: 1
ORIGINALITY: 2
DATE: 2025-06-26

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors address the problem of surface reconstruction from a 3D Gaussian Splatting scene. They introduce several losses using geometrical and multi-view cues to improve the reconstruction. The method achieves better reconstruction performance, albeit at a longer optimization time. The evaluation is conducted by comparing the surface reconstruction with a broad selection of related methods, including NeRF and Gaussian splatting approaches on several commonly accepted datasets.\n\nSTRENGTHS AND WEAKNESSES:\n**Strenghts**\nThe paper presents a clear mathematical formulation of the proposed loss terms, accompanied by a well-reasoned justification for how these additions enhance the geometric refinement of the Gaussians. The authors argue convincingly that the introduced terms address the limitations of purely photometric supervision by enforcing geometric consistency. The method is thoroughly evaluated through both quantitative and qualitative comparisons against a broad set of related approaches.\n\n**Weaknesses**\nThe paper suffers from several issues that affect its clarity and coherence. \n\nFor example:\n1. In Section 5.2, a scale loss is introduced in the ablation study without any prior mention or explanation in the method section. \n2. Placing result tables within the method section disrupts the logical flow of the paper and blurs the line between methodological description and experimental validation.\n\nIn terms of performance, the proposed method reports improved Chamfer distances in 9 out of 14 scenes on the DTU dataset, as shown in Table 1. However, these gains come at the cost of significantly longer training times - 15.5 minutes compared to 3.4 minutes for 3DGS, which diminishes the impact of the reported improvements, especially when they are relatively modest.\n\nMoreover, although the title emphasizes multi-view alignment, the actual performance gains attributed to multi-view supervision are minimal. The increase in Precision, Recall, and F1-score when scaling from a single view (N=1) to four views (N=4) is only +0.02, which raises doubts about the effectiveness of the method’s multi-view strategy and the strength of its central claim.\n\nQUESTIONS:\nCould you explain how the recall in Table 4 is determined? Is there something classified (threshold of distance, which one was used)? While referring to previous work, it would make the paper more complete if you explained how you calculated it and why it matters.\n\nHow is the ablation in 5.2 done? Are the other terms rescaled, or is each term just dropped? Are the iterations the same?\n\nLine 221: “Additionally, due to lighting variations, the color of the same surface point may differ across views, making photometric consistency unreliable.” - Why is this a problem for geometry? Don't spherical harmonics address exactly this?\n\nLIMITATIONS:\nThe primary limitation is the optimization time required by the method. One might question whether such a small gain in reconstruction quality is worth the resources spent on optimization.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a method to enhance surface reconstruction in 3D Gaussian Splatting (3DGS) by introducing geometry-aware constraints into the rendering process. The authors incorporate four novel constraints: (1) an edge-aware image reconstruction loss that sharpens object boundaries, (2) a normal-based geometry alignment loss that refines surface orientation while suppressing noise and over-smoothing, (3) a multi-view photometric alignment loss that enforces visibility-aware color consistency across views using local homographies, and (4) a feature alignment loss based on deep features to handle variations in appearance (e.g., due to illumination change). The final objective combines all these losses to jointly optimize geometry and appearance. The method achieves state-of-the-art results on standard benchmarks in both surface reconstruction and novel view synthesis.\n\nSTRENGTHS AND WEAKNESSES:\n--- Strengths ---\n\n- The paper is well-executed, with clear ideas and thorough descriptions.\n- The proposed loss functions are sensible and appear to contribute meaningfully to improving geometric accuracy.\n- The experimental section convincingly demonstrates the benefits of the proposed method on standard datasets.\n\n--- Weaknesses ---\n\nI did not identify any major flaws in the paper.\n\n- The most notable weakness is the significantly increased run-time compared to standard 3DGS. This is expected, given the complexity of the proposed losses. It would be valuable to provide an approximate run-time for each loss. For instance, according to Table 4, \nL_f  has only a marginal impact on geometric accuracy, so understanding its computational cost would help assess the trade-off.\n- The ablation study reports only geometric errors; including photometric errors would provide a more complete evaluation.\n- Figure 2 is not informative in its current form. It is unclear where the process begins or how the components interact. I recommend redrawing the figure to make the pipeline clearer.\n\nQUESTIONS:\nI am mostly interested in the run-time cost of each introduced loss.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes VA-GS, a method for enhancing the geometric representation of 3D Gaussian Splatting via view alignment. The approach incorporates five key loss functions to improve surface reconstruction: edge-aware image reconstruction, normal-based geometry alignment, visibility-aware multi-view photometric alignment, and multi-view feature alignment. The method addresses limitations in boundary delineation and illumination-induced artifacts that affect surface quality in standard 3DGS. Experiments on DTU and TNT datasets demonstrate improved surface reconstruction metrics while maintaining novel view synthesis quality.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1. The paper addresses important limitations of 3DGS by incorporating multiple complementary constraints - edge awareness, normal consistency, photometric alignment, and feature-level consistency - creating a well-rounded solution for geometric enhancement.\n\n2. The method integrates cleanly with existing 3DGS frameworks without requiring architectural changes, making it easily adoptable.  For instance, the visibility-aware photometric alignment is particularly well-designed for handling occlusions.\n\n3. Comprehensive evaluation on standard benchmarks (DTU, TNT, Mip-NeRF 360) shows consistent improvements in surface reconstruction metrics while maintaining competitive novel view synthesis performance.\n\nWeakness\n\n1. Novelty concern.  The proposed technique resembles prior methods such as PGSR and PSDF. While the combination is effective, individual components (edge-aware losses, normal consistency, photometric alignment) are well-established techniques. \n\n2. Missing comparison with PSDF.  Despite being directly related in spirit, the paper does not compare VA-GS with more advanced methods such as PSDF.\n\nQUESTIONS:\nI suggest the author to include comparisons with PSDF to better position the contribution and justify the novelty concern.\n\nLIMITATIONS:\nPlease refer to Questions and Weakness.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}