Title: SAP: Exact Sorting in Splatting via Screen-Aligned Primitives
Venue: NeurIPS 2025
Accepted: yes
Keywords: 3DGS; NeRF;Novel View Synthesis
Abstract: Recently, 3D Gaussian Splatting (3DGS) has achieved state-of-the-art rendering results. However, its efficiency relies on simplifications that disregard the thickness of Gaussian primitives and their overlapping interactions. These simplifications can lead to popping artifacts due to inaccurate sorting, thereby affecting the rendering quality. In this paper, we propose Screen-Aligned Primitives (SAP), an anisotropic kernel that generates primitives parallel to the image plane for each view. Our rasterization pipeline enables full per-pixel ordering in real time. Since the primitives are parallel for a given viewpoint, a single global sorting operation suffices for correct per-pixel depth ordering. We formulate 3D reconstruction as a combination of a 3D-consistent decoder and 2D view-specific primitives, and further propose a highly efficient decoder to ensure 3D consistency.  Moreover, within our framework, the primitive function values remain consistent between view space and screen space, allowing arbitrary radial basis functions (RBFs) to represent the scene without introducing projection errors. Experiments on diverse datasets demonstrate that our method achieves state-of-the-art rendering quality while maintaining real-time performance.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Screen-Aligned Primitives (SAP), a new rendering framework that solves the depth-sorting problem in 3D Gaussian Splatting. Instead of using 3D Gaussians that often overlap and require approximate sorting, SAP predicts view-dependent 2D Gaussians that are always aligned with the image plane. This allows exact per-pixel depth sorting.\nThe primitives are predicted from a set of 3D anchor Gaussians using a lightweight decoder network, which is conditioned on the viewing direction and some input features. \nExperiments show that SAP achieves state-of-the-art rendering quality with real-time performance, eliminating artifacts like popping, while maintaining a consistent and compact 3D representation.\n\nSTRENGTHS AND WEAKNESSES:\n+) SAP addresses a well-known issue in Gaussian Splatting: the incorrect ordering of overlapping Gaussians, which leads to popping artifacts due to sorting based on the center position rather than per-pixel depth. In contrast to concurrent methods like EVER, which perform per-pixel sorting at the cost of speed, SAP proposes a neural network that predicts view-dependent 2D Gaussians aligned with the camera's image plane. As a result, a simple global sort by the primitive centers yields correct per-pixel ordering. This approach is effective, and it leads to improved visual quality over prior work. \nThe figures are clear and well-designed, making the core ideas and architecture easy to understand.\nOverall, this is a good submission that improves upon existing methods while maintaining conceptual clarity.\n\n-) SAP introduces view-dependent primitives, meaning the set of rendered splats (and their properties) change with the camera angle. This is handled by the learned 3D-consistent decoder, but it also adds complexity and potential points of failure. If the decoder does not perfectly maintain consistency, there could be flickering or popping of primitives when the viewpoint changes.\n\n-) By relying on a neural network to predict Gaussians conditioned on the camera direction, the method moves away from an explicit 3D representation populated with fixed Gaussians. Instead of maintaining a globally defined set of 3D primitives, the scene is now implicitly represented through a view-conditioned function, making it harder to reason about or extract a concrete 3D structure. In addition, the improvements over other baselines remain relatively marginal in terms of standard quantitative metrics such as PSNR, SSIM, and LPIPS. \n\n-) The approach does not incorporate surface normals or an explicit surface prior, focusing only on rendering quality rather than geometric fidelity. The authors acknowledge that, in the absence of normal constraints, extracting a mesh from the representation is difficult. While mesh extraction is beyond the scope of novel view synthesis, this limitation reduces the applicability of SAP in scenarios that require explicit 3D geometry (though this is a minor concern given the paper’s emphasis on image-based rendering). It would be interesting for future work to explore ways of integrating smooth surface constraints, such as learned normals,... , to enable consistent and accurate mesh extraction from the representation. Are there any ideas on how future work could incorporate smooth surface constraints to enable mesh extraction?\n\n-) In my opinion, the year and conference are not important or necessary to include in the table. I would recommend removing them and instead adding metrics such as memory usage, training time, and inference time (as presented in the 3DGS paper), as these offer significantly more meaningful insights into the performance of the methods.\n\n-) I would also add Zip-NeRF as one of the baselines, as it is currently considered the state of the art on the Mip-NeRF360 dataset.\n\n-) Some minor comments:\n\nL248: Typo — \"Implenmentation Details\" should be corrected to \"Implementation Details.\"\n\nL129: I would avoid starting the section with “To overcome this limitation,” as the specific limitation being referred to is not clearly introduced in this section, and the reader may not understand what it refers to.\n\nThis is just a personal preference, but I would suggest placing the qualitative results of SAP next to the ground truth. This would make visual comparisons easier for the reader.\n\nQUESTIONS:\n1. The method does not model surface normals or enforce geometric priors, making it difficult to extract meshes. Do the authors have an idea on how to integrate smooth surface constraints to enable mesh extraction with SAP?\n\n2. The tables include publication year and venue, which are not particularly informative for evaluating method performance. I recommend replacing them with the memory consumption, training time, and inference time, as done in the original 3DGS paper.\nIncluding such metrics would better support the real-time rendering claims and improve the empirical comparison.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-04"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nOverall, this submission focuses on addressing the popping artifects in 3D-GS. It proposes to deal with this via following Scaffold-GS's anchor-based pipeline and further guiding the generation of screen-aligned primitives.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n\nThis submission is easy to follow. Meanwhile, the reviewer also feels that the methods proposed hold their merits.\n\nWeaknesses:\n\n(See the questions section for more details).\n\nQUESTIONS:\nOverall, the reviewer currently votes for borderline accept while he still has the following concerns.\n\n1. The authors propose to use parallel thin slice instead of 3D Gaussians as their primitives and show some appealing properties of this choice. Despite this, intuitively, parallel thin slices can still be regarded as primitives that has less representation freedom than 3D Gaussians. The reviewer is interested to see more discussion from this perspective.\n\n2. For example, one potential disadvantage of parallel thin slices can be that their multi-view inconsistency. While the authors claim here and there that they use a \"3D-consistent\" decoder. The reviewer is still a bit confused about how 3D (multi-view) consistency can be guaranteed for parallel thing slices. The authors are appreciated to theoretically (or at least emphrically) explain this part.\n\n3. In the related works, the authors mention that those methods in the early stage ([44, 45]) do not suffer from popping artifacts. Considering this, the authors are appreciated to better discuss, why such ideas cannot be directly incorporated into the recent 3DGS framework to handle popping artifacts?\n\n4. In line 129, the \"this\" in \"to overcome this limitation\" seems to point to nowhere.\n\n5. in Section 4.1, the authors mention that they decide to use SH for encoding. Yet, one typical problem in SH can be there parameter demanding property (e.g., when used to represent color for Gaussian kernels in 3D-GS). In this case, the reviewer is wondering if the usage of SH in this method's context also suffer it from high memory overhead?\n\n6. In line 167, the authors mention that the activation is \"such as ReLU\". Such ambiguity is not good for reproduction and thus authors are thus suggested to make this part more clear.\n\nIn summary, the reviewer currently votes for borderline accept for this submission while still holding the above concerns.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-10"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces Screen-Aligned Primitives (SAP) to improve 3D Gaussian Splatting rendering. The primary issue with 3DGS is \"popping artifacts\" caused by an inaccurate sorting of primitives that doesn't account for their thickness and overlapping regions. To solve this, the authors propose a method that generates primitives constrained to be parallel to the image plane for any given viewpoint. Because all primitives are parallel, a single global depth sort becomes sufficient to achieve a correct per-pixel rendering order, fundamentally eliminating the sorting problem without extra computational cost. The framework uses a neural decoder to generate these view-specific 2D primitives conditioned on anchor features and viewing direction, which allows for an error-free projection and the use of more flexible rendering kernels.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n+ The paper is well-motivated and clearly written, making its contributions easy to understand. \n+ The core idea of using Screen-Aligned Primitives is straightforward and intuitive. \n+ This approach is conceptually similar to multi-plane image (MPI) based rendering, providing a strong rationale for why it effectively eliminates overlap artifacts and allows for a simple, global sorting pass.\n\nWeakness:\nThe method's strong similarity to multi-plane image (MPI) based rendering suggests a potential limitation, common to such approaches, which is its dependency on the training data's density and distribution. The framework's performance is likely to be strongest when rendering novel views that are interpolated between the dense viewpoints seen during training. However, in the qualitative results, authors did not illustrate the distribution of training and testing views, the video demos in supp are also rendered from simply trajectories. Similarly, the approach may struggle if the training data itself is sparse, as the multiplane-based representation might not generalize well across large, unseen gaps between viewpoints. The paper does not fully investigate these challenging scenarios.\n\nQUESTIONS:\n- The ablation study in Table 2 shows a curious inconsistency where Screen-Aligned Primitives improve PSNR over the baseline, yet score worse on SSIM and LPIPS in most intermediate configurations, which warrants a deeper analysis of the qualitative trade-offs between the two representations. \n\n- To better understand the method's generalization capabilities, a robustness study would be valuable, such as evaluating performance with sparser input views, especially given the framework's similarity to multi-plane image rendering which can be sensitive to training data density.\n\nLIMITATIONS:\nSee questions and weakness.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the problem of depth-sorting inaccuracies in 3D Gaussian Splatting. Depth-sorting inaccuracies cause popping artifacts and rendering inconsistencies due to overlapping anisotropic Gaussians. The paper proposes Screen-Aligned Primitives (SAP) as a solution. SAP generates per-view, plane-parallel primitives to eliminate per-pixel overlap errors and enables exact sorting without additional computational cost. The design is supported by a 3D-consistent decoder and flexible kernel choices. It is validated through comprehensive experiments, which show improved rendering quality and real-time performance.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n-- The proposed approach is intuitive and easy to understand. Introducing screen-aligned primitives allows per-pixel ordering with low computational cost, which well addresses the depth-sorting issue in 3D Gaussian Splatting. \n\n-- The method is compatible with existing frameworks and supports flexible kernel designs without projection bias. \n\n-- Experimental results on several benchmarks show that the method improves rendering quality and consistency compared to related baselines.\n\n\nWeaknesses:\n\n-- The method assumes that all primitives can be well approximated as screen-parallel slices. This may not be always true, limiting its adaptability to highly complex or non-Lambertian scenes. \n\n-- The current design focuses mainly on radial basis kernels and does not yet explore asymmetric or more diverse kernel shapes. Some implementation details, such as potential trade-offs between sorting precision and densification cost, could be elaborated further.\n\nQUESTIONS:\n1. How does the proposed screen-aligned primitive design handle highly complex geometry or scenes with strong non-Lambertian appearance, where local shape details or view-dependent reflections may challenge the planarity and directional consistency assumptions? More experiments and discussion are needed to clarify how the method performs for materials with transparent or semi-transparent properties, which may not be well approximated by a single opaque plane.\n2. Could the authors discuss how the method performs in scenes with large depth ranges or multiple layers of fine occlusion, where a single global sorting order may not fully resolve local ordering conflicts?\n3. Clarify how the maximum-gradient densification strategy deals with noisy regions or high-frequency texture boundaries, and whether it risks adding unnecessary primitives that increases computation without clear quality benefits.\n4. Please provide more information on training and inference memory usage, runtime cost, and possible trade-offs between sorting precision.\n\nLIMITATIONS:\nThe paper discusses the main limitations and outlines possible directions for future improvement.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}