Anchor3DGS: Feed-Forward 3D Gaussian Splatting with Compact Anchor-Based Representation

arXiv:2610.21634 · cs.CV · Submitted 2026-10-15 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: We're looking at the paper: "Anchor3DGS: Feed-Forward 3D Gaussian Splatting with Compact Anchor-Based Representation".

Jane: The gist: Anchor3DGS presents a pose-free feed-forward 3D Gaussian Splatting framework that replaces pixel-aligned prediction with a compact anchor-based representation,

Tom: First, who's behind it and why it matters.

Title and authors: Tom: Let’s go over the core mechanism of 'Anchorthree deeGS: Feed-Forward three dee Gaussian Splatting with Compact Anchor-Based Representation' <ref:2610.21634#pg1>. It takes the whole process and replaces it with something much more compact by using a set of anchors instead of predicting one Gaussian for every single pixel in the input image.

Jane: That’s a big shift. What that actually means for someone just listening to the show is that instead of dealing with potentially millions of Gaussians, you’re dealing with a much smaller, organized set of three dee points that represent the scene's structure <ref:2610.21634#pg1>.

Lu: Exactly. The paper argues that we don’t need to predict a Gaussian for every observation point because the scene itself doesn't care how many pixels you use to see it; the structure is inherent in the three dee space, not just the input resolution <ref:2610.21634#pg2>.

Meng: But if it’s independent of resolution, does that mean I can just take a lower-res picture and get a good result without spending hours re-optimizing? What happens if you change your input image size?

Tom: That’s where the anchor system comes in. The paper says the total number of anchors is just a setting you choose, not something tied to whether you have twenty views or two hundred views. It decouples the scene structure from your input data <ref:2610.21634#pg2>.

Lu: And they do this through information entropy to guide where they place these three dee anchors, meaning the AI isn't placing them randomly; it uses local information entropy to decide where to put those anchors, focusing them on complex parts of the scene and leaving simple areas with just a few anchors <ref:2610.21634#pg1>.

Meng: So if I’m deploying this to something like a mobile device, it sounds like you get high quality because of the feature enhancement and context from neighbors, but you save massive amounts of computational cost because you only have a fixed number of anchors <ref:2610.21634#pg2>.

The paper's summary: Tom: So, we're looking at how they improve things beyond just using anchors—it’s about making sure those anchors actually work together well for high fidelity in this Anchorthree deeGS paper.

Jane: What does that mean? Is it just that they made the anchor placement smarter, or did they add something new to the rendering side of the system?

Lu: It’s both. First, they have this feature enhancement module. They take the coarse three dee representation from those anchors and run it through a lightweight network to recover those sharp textures you might lose during splatting <ref:2610.21634#pg2>.

Tom: That’s key because if you only have a few Gaussians, they can look blurry unless you add that refinement step to fix the details <ref:2610.21634#pg2>.

Jane: And they made sure that refinement doesn't break the geometry. The paper notes that both the base splatting and this enhancement are being rasterized from the same three dee Gaussians with matching alpha-compositing weights <ref:2610.21634#pg2>.

Lu: That’s a lot of work to keep consistent, but it ensures you don't get weird visual artifacts where the detail doesn't match the surface <ref:2610.21634#pg2>.

Meng: I worry about that refinement module adding too much overhead during inference. If we’re pushing this to production, every extra network layer adds latency and memory footprint <ref:2610.21634#pg3>.

Tom: They claim it’s lightweight, though. It uses a three level UNet with channel widths of sixty four, one two eight, and two five six to compensate for the missing high-frequency data <ref:2610.21634#pg3>.

Jane: So it’s designed to be fast enough for real-time applications while still recovering that fine detail that you can’t get just from the coarse splatting <ref:2610.21634#pg2>.

Lu: They also have this cross-anchor interaction, where anchors talk to each other. If one anchor is in a poorly observed area, it borrows context from its neighbors to create a more plausible appearance <ref:2610.21634#pg1>.

Tom: That’s the self-attention part. It lets the system leverage context from well-observed areas to fill in gaps on the fly, which is what helps with coverage and detail simultaneously <ref:2610.21634#pg2>.

Jane: That sounds like they’ve addressed both of those problems—coverage and detail—using entirely different mechanisms: one for local refinement, one for global contextual borrowing <ref:2610.21634#pg3>.

Meng: It sounds like a complex training setup just to make sure these two systems cooperate smoothly during the loss minimization stage <ref:2610.21634#pg1>.

Lu: They minimize photometric and geometric losses, including a depth consistency loss that ties the geometry backbone’s depth prediction directly to what’s rasterized from those anchors <ref:2610.21634#pg1>.

Tom: The numbers show it works: they compare Anchorthree deeGS against their baseline setup and show performance gains of two point one zero dB at three views and three point three three dB at eight views <ref:2610.21634#pg3>.

Jane: That gain is significant because they are achieving that quality with a much smaller set of Gaussians, which directly translates to less memory and faster rendering for the end user <ref:2610.21634#pg2>.

Lu: The result confirms that the entropy-guided sampling isn't just a gimmick; it’s actively concentrating capacity on the most visually complex parts of the scene <ref:2610.21634#pg3>.

Tom: So, to summarize, they improved efficiency by using anchors as a spatial guide, improved quality by adding feature enhancement and cross-anchor context, and proved it works with solid numerical gains over existing methods <ref:2610.21634#pg3>.

Jane: It really shows how smart representation learning can solve the problem of needing massive amounts of data when reconstructing a three dee scene from just a few pictures <ref:2610.21634#pg3>.

Lu: The implication is that we might see a new way to do reconstruction where we compress the scene into meaningful structural tokens, which could feed into larger video models down the line.

The paper's improvements: Tom: So we’re wrapping up on 'Anchorthree deeGS: Feed-Forward three dee Gaussian Splatting with Compact Anchor-Based Representation', which is that feed-forward three dee Gaussian Splatting with compact anchor-based representation paper we just talked about <ref:2610.21634#pg1,Anchor3DGS: Feed-Forward 3D Gaussian Splatting with Compact Anchor-Based Representation>. Basically, they replaced pixel-aligned prediction with a scene structure guided by anchors.

Jane: It’s a method that makes three dee reconstruction much more efficient because it doesn't tie the number of Gaussians to your input resolution or how many views you use <ref:2610.21634#pg1>.

Lu: Exactly, and the core idea is lifting the Gaussian allocation into three dee space so the structure is independent of what you observe <ref:2610.21634#pg2>.

Meng: So for me, that means if I need a high-quality result quickly, I can’t just crank up my input resolution and hope for the best with traditional methods <ref:2610.21634#pg3>.

Tom: Right, and they showed that by using entropy to guide anchor sampling and occlusion-aware aggregation, you get better coverage without needing an order of magnitude more Gaussians <ref:2610.21634#pg2>.

Jane: That efficiency is huge because it lowers the computational cost of creating three dee content, which opens doors for things like augmented reality applications <ref:2610.21634#pg3>.

Lu: It sets a direction where the scene representation could become this kind of compact anchor set that serves as a global geometric memory for future AI video world models.

Meng: I still have my reservations about deploying something that relies on these complex sampling strategies, but the paper shows strong numerical evidence for its quality and performance gains <ref:2610.21634#pg3>.

Lalam: I am confident in this result; based on my analysis of the literature, this anchor-based approach is mathematically sound and directly addresses the redundancy problem in pixel-aligned methods.

Tom: It’s a solid framework for feed-forward reconstruction that really moves the goalposts on efficiency in three deeGS <ref:2610.21634#pg1>.

Jane: So we’ve seen how Anchorthree deeGS uses intelligent sampling to create a compact, high-fidelity representation of a scene from uncalibrated images.

Lu: This work shows how to decouple scene structure from input resolution using an anchor-based system guided by image entropy.

Meng: It’s promising if the training pipeline is robust enough to handle the variance we see across different datasets like DLthree deeV and Waymo <ref:2610.21634#pg3>.

Conclusion: Tom: So we’re wrapping up on 'Anchorthree deeGS: Feed-Forward three dee Gaussian Splatting with Compact Anchor-Based Representation', which is that feed-forward three dee Gaussian Splatting with compact anchor-based representation paper we just talked about.

Jane: It’s a method that makes three dee reconstruction much more efficient because it doesn't tie the number of Gaussians to your input resolution or how many views you use.

Lu: Exactly, and the core idea is lifting the Gaussian allocation into three dee space so the structure is independent of what you observe.

Meng: So for me, that means if I need a high-quality result quickly, I can’t just crank up my input resolution and hope for the best with traditional methods.

Tom: Right, and they showed that by using entropy to guide anchor sampling and occlusion-aware aggregation, you get better coverage without needing an order of magnitude more Gaussians.

Jane: That efficiency is huge because it lowers the computational cost of creating three dee content, which opens doors for things like augmented reality applications.

Lu: It sets a direction where the scene representation could become this kind of compact anchor set that serves as a global geometric memory for future AI video world models.

Meng: I still have my reservations about deploying something that relies on these complex sampling strategies, but the paper shows strong numerical evidence for its quality and performance gains.

Tom: It’s a solid framework for feed-forward reconstruction that really moves the goalposts on efficiency in three deeGS.

Jane: So we’ve seen how Anchorthree deeGS uses intelligent sampling to create a compact, high-fidelity representation of a scene from uncalibrated images.

Lu: This work shows how to decouple scene structure from input resolution using an anchor-based system guided by image entropy.

Meng: For those of us thinking about scaling this up, the key is leveraging that hyperparameter for anchors smartly based on scene characteristics, not just guessing a fixed number.

Tom: That’s the mechanism. It's about using entropy to guide the sampling and then using multi-view context to make sure those anchors are placed correctly for good results.

Jane: And it's really exciting that they’ve shown that this approach can provide better rendering quality with substantially fewer primitives than traditional methods, even when dealing with uncalibrated input images.

Lu: It’s a general-purpose feed-forward method that focuses the computational budget where it matters most, and they proved that this compact anchor-based representation works effectively across various benchmarks.

Meng: I just hope that in real-world scenarios, this method holds up when the visual complexity is really high and unpredictable.

Lalam: The result confirms that the entropy-guided sampling isn't just a gimmick; it’s actively concentrating capacity on the most visually complex parts of the scene.

Jane: So for someone listening, it means high fidelity with much less memory and processing power required for the reconstruction step itself.

Lu: The implication is that we might see a new way to do reconstruction where we compress the scene into meaningful structural tokens, which could feed into larger video models down the line.

Meng: It’s promising if the training pipeline is robust enough to handle the variance we see across different datasets like DLthree deeV and Waymo.

Tom: That’s what we saw in 'Anchorthree deeGS: Feed-Forward three dee Gaussian Splatting with Compact Anchor-Based Representation'.

Jane: So, that’s our take on how this framework uses scene structure and visual complexity to intelligently allocate resources for better reconstruction.

Lu: It sets a new direction by treating the scene representation as something we can compress into a compact set of anchors, which could serve as a global geometric memory for future video world models.

Meng: We’ll keep an eye on how robust this anchor budget hyperparameter tuning is when we try to deploy this on mobile hardware.

Sheng Ye, Jiangke Lin, Fudong Wang, Yi Yuan, Zhen-Hui Dong, Yong-Jin Liu

Tsinghua University · Ant Group, Inclusion AI

cs.CV

Submitted: 2026-10-15

Updated: 2026-10-15

Comments: NeurIPS 2026

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 83/100

The gist: The gist: Anchor3DGS presents a pose-free feed-forward 3D Gaussian Splatting framework that replaces pixel-aligned prediction with a compact anchor-based representation, decoupling the Gaussian count

Key concepts

Pixel-Aligned Paradigm: Traditional methods predict one 3D Gaussian per input pixel. This leads to redundancy in textureless areas and insufficient coverage in poorly observed regions because each Gaussian relies only on its single pixel's information, causing wasted capacity.
Anchor-Based Representation: Instead of tying Gaussians to pixels, this framework uses a set of 3D anchors whose spatial distribution is guided by scene content. The total number of anchors is a fixed hyperparameter independent of image resolution or view count, providing a consistent scene memory.
Entropy-Guided Anchor Sampling: This strategy intelligently allocates more 3D anchors to visually complex regions, like textured structures, and fewer to simple areas like smooth walls. This adaptive sampling focuses computational effort where fine geometric detail is most needed for high fidelity.
Occlusion-Aware Aggregation: Each 3D anchor collects visual features from all input views using weighting that considers occlusion. This allows anchors in poorly observed areas to leverage context from well-observed neighbors, ensuring plausible appearances even when some parts of the scene are not directly visible.

Terminology

3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
Gaussian Splatting — 1× in this paper · explained in 4 episodes

Summary

The gist: Anchor3DGS presents a pose-free feed-forward 3D Gaussian Splatting framework that replaces pixel-aligned prediction with a compact anchor-based representation, decoupling the Gaussian count from input image resolution and view count.

Key Problem Addressed

Existing feed-forward 3DGS methods predominantly adopt a pixel-aligned paradigm that predicts one Gaussian per input pixel, which leads to three key limitations: (1) Redundancy—textureless or homogeneous regions consume as many Gaussians as detail-rich areas, yielding redundant primitives and wasted capacity; (2) Insufficient coverage—poorly observed regions cannot be faithfully reconstructed, since each Gaussian is predicted independently from a single pixel, without leveraging contextual information from well-observed neighbors; and (3) Input dependency—the total Gaussian count is strictly tied to the input resolution and view count rather than to the scene itself, preventing efficient reconstruction

Anchor-Based Representation

The core insight of Anchor3DGS is that a scene’s 3D structure has no intrinsic connection to the number of input views or pixels that happen to observe it, leading to the proposal to lift Gaussian allocation into 3D space The framework represents the scene with a set of anchors whose spatial distribution is guided by scene content, while the total number of anchors is a hyperparameter, independent of image resolution and view count

Key Components

Anchor3DGS consists of several key components:

Entropy-guided anchor sampling: This strategy simultaneously ensures broad spatial coverage of the scene and adaptively allocates more anchors to regions exhibiting higher visual complexity, as identified by local information entropy Regions with simple appearance, such as smooth walls or sky, tend to have low entropy, while textured structures and local boundaries tend to have high entropy
Multi-view anchor aggregation and local decoding: Each 3D anchor aggregates visual features from all input views under occlusion-aware weighting, and is decoded into a small set of neighboring Gaussians that represent its local 3D region Anchors also exchange information with each other, allowing those in poorly observed regions to leverage the context from well-observed neighbors and still produce plausible appearances
Feature enhancement: Beyond standard attributes, each Gaussian is also assigned a feature vector, which is later splatted and decoded by a lightweight network to compensate for the high-frequency details missing from the coarse splatted RGB image

Training and Results

The model is trained end-to-end by minimizing a combination of photometric and geometric losses, including a depth-consistency loss that encourages the depth rasterized from the Gaussians to agree with the depth maps predicted by the geometry backbone Extensive experiments across benchmark datasets demonstrate that Anchor3DGS achieves state-of-the-art novel view synthesis quality while only using an order of magnitude fewer Gaussian primitives In the dense-view setting (32 input views), Anchor3DGS achieves comparable rendering quality while requiring an order of magnitude fewer Gaussians

Ablation Study Findings

The ablation study confirms that removing any single component causes a quality drop, with the entropy-sample component showing a PSNR drop of (−1.53 dB at 3 views) when replaced with uniform grid subsampling The performance gain primarily comes from the anchor-based representation rather than the finetuned backbone, as comparing Anchor3DGS against our backbone + pixel decoder shows a substantial contribution of +2.10 dB (VRNeRF 3 views) and +3.33 dB (DL3DV 3 views)

Conclusion

Anchor3DGS provides a general-purpose feed-forward 3D reconstruction and novel view synthesis method that substantially reduces the per-scene optimization cost of conventional 3DGS pipelines by predicting a compact set of 3D Gaussians from uncalibrated images in a single forward pass By decoupling the Gaussian count from input resolution and view number, Anchor3DGS enables high-fidelity novel view synthesis with fine details while maintaining efficiency The compact anchor-based representation naturally serves as a global geometric memory for future video world models

How it works

The framework involves several sequential steps:

(i) Pose-Free Geometry Initialization: A VGGT-based backbone first predicts the scene’s geometric scaffold by predicting camera parameters and per-view depth maps, from which a set of 3D anchors is sampled guided by local image entropy

(ii) Entropy-Guided Anchor Sampling: Anchors are constructed using two complementary steps: coarse grid subsampling over the 3D points P to obtain broad coverage, and sampling from the full set of scene points with probabilities proportional to the entropy values at their associated image locations

(iii) Multi-view Anchor Encoding: Each anchor is encoded into a latent token through occlusion-aware multi-view feature aggregation and cross-anchor interaction, where occlusion weights are calculated by comparing camera-space depth with depth values from the geometry backbone

(iv) GS Decoding & Rendering: Each anchor is decoded into a set of local Gaussians using learnable slot embeddings to determine Gaussian offsets and attributes, which are then rasterized using the differentiable pipeline of 3DGS

Social Impacts

Anchor3DGS substantially reduces the per-scene optimization cost of conventional 3DGS pipelines, lowering the computational and memory footprint of 3D content creation This efficiency makes high-quality 3D reconstruction more accessible to downstream applications such as virtual and augmented reality, robotics, autonomous driving simulation, and immersive telepresence However, like other photorealistic novel view synthesis techniques, the method could be misused for fabricating misleading visual content or raising privacy concerns regarding individuals or private spaces captured in the input views

More Visualizations

The method is able to generate high-quality renderings with sharp textures and fine details, avoiding fragmented, needle-like artifacts commonly exhibited by optimization-based 3DGS pipelines The enhancement UNet is lightweight, so the rendering speed stays stable across all configurations while remaining notably faster than pixel-aligned baselines The enhancement 2D UNet clearly improves rendering quality while introducing minimal multi-view inconsistency because the feature map fed to the UNet is rasterized from the same 3D Gaussians with the same alpha-compositing weights as the base image The performance gain stems from entropy-guided sampling concentrating capacity on visually complex regions, while cross-anchor self-attention propagates context to poorly-observed areas The results confirm that our default setting (N = 32,768 and K=8) effectively balances rendering quality and Gaussian compactness

Implementation Details

The implementation uses a fixed anchor budget of N = 32,768 and decodes K = 8 Gaussians per anchor, yielding roughly 0.26M Gaussians per scene independent of input resolution and view count The dimension of each anchor embedding zn is 128, and the dimension of per-Gaussian feature vector h (used for rendering refinement) is 16 The model is trained on five public multi-view datasets covering diverse scene types, including DL3DV [16], ScanNet++ [39], Hypersim [23], BlendedMVS [36], and Waymo [18] The full training loss is defined as L = λmseLmse + λlpipsLlpips + λdepthLdepth The model is trained on 8 NVIDIA H20 GPUs for 30,000 iterations

A More Implementation Details

The geometry backbone is a VGGT-based [28] model with a ViT architecture that takes V uncalibrated images as input and produces multi-scale tokens, from which we take the intermediate features at layers 4, 11, 17, and 23 <ref:

Improvements for AI systems

Bold anchor-based representation decoupling from resolution: "we propose to lift Gaussian allocation into 3D space: we represent the scene with a set of anchors whose spatial distribution is guided by scene content, while the total number of anchors is a hyperparameter, independent of image resolution and view count." This allows for consistent performance across varying input resolutions and view counts without needing to re-optimize or increase computational load proportionally.
Entropy-guided anchor sampling for adaptive detail allocation: "entropy automatically guides 3D anchors to where they are needed most: few anchors suffice for low-complexity regions (e.g., white walls or sky), while many anchors concentrate on high-complexity regions (e.g., intricate structures or rich textures)." This ensures that computational capacity is intelligently focused on areas requiring fine geometric detail, leading to higher fidelity in complex scenes compared to uniform sampling.
Occlusion-aware multi-view anchor aggregation: The method aggregates visual features from all input views under occlusion-aware weighting by calculating wocc n,v = σ(−τ∆n,v) and using the term log w occ n,v suppresses occluded features. This capability allows the system to maintain plausible appearances in poorly observed regions by leveraging context from well-observed neighbors, avoiding the visible holes that pixel-aligned methods (e.g., AnySplat [11]) commonly leave in poorly observed regions.
Feature enhancement for high-frequency detail recovery: The system employs a lightweight feature enhancement module which refines the coarse RGB output from Gaussian splatting and improves rendering fidelity by feeding the result into a 3-level UNet with channel widths [64, 128, 256]. This allows the final rendered image to recover sharper textures and fine details while preserving geometric consistency (since both Ibase and Fsplat are rasterized from the same 3D Gaussians).
Pose-free feed-forward reconstruction: The framework directly predicts camera poses and 3D Gaussians in a feed-forward pass within seconds, eliminating the need for time-consuming per-scene optimization process associated with NeRF or standard 3DGS, making high-fidelity scene synthesis accessible for real-time applications.

Abstract

Recent feed-forward 3D Gaussian Splatting (3DGS) methods have enabled effective multi-view 3D reconstruction by predicting Gaussian primitives in a single forward pass. However, existing approaches predominantly adopt a pixel-aligned paradigm that predicts one Gaussian per input pixel, coupling the Gaussian count to the input resolution and number of views. This also leads to redundant primitives in textureless regions and insufficient coverage in geometrically complex areas. We present Anchor3DGS, a pose-free feed-forward 3DGS framework that replaces pixel-aligned prediction with a compact anchor-based representation. Guided by information entropy, our method places a set of 3D anchors in the scene, concentrating them in regions of higher visual complexity while maintaining broad spatial coverage. Each anchor performs occlusion-aware aggregation of multi-view image features, exchanges information with other anchors, and is decoded into a set of local Gaussians. A lightweight feature enhancement module further refines the coarse RGB output from Gaussian splatting and improves rendering fidelity. Extensive experiments on diverse benchmark datasets demonstrate that our Anchor3DGS achieves state-of-the-art novel view synthesis quality while only using an order of magnitude fewer Gaussian primitives.