ConfidentSplat: Confidence-Aware 3D Gaussian Splatting

• ConfidentSplat is a framework of confidence-aware techniques in 3D Gaussian Splatting that enhances both compression and SLAM through explicit reliability signals.
• One approach leverages learnable Beta distributions as per-splat confidence to guide test-time pruning, balancing fidelity with reduced computational cost.
• Another method applies confidence-weighted depth fusion in RGB-only SLAM, significantly improving geometric accuracy by fusing multi-view depth with monocular priors.

ConfidentSplat denotes confidence-aware methodologies in Gaussian Splatting that use explicit reliability signals to control either representation compression or geometric supervision. In 2025, the term referred to two distinct research directions: "Confident Splatting: Confidence-Based Compression of 3D Gaussian Splatting via Learnable Beta Distributions" (Razlighi et al., 28 Jun 2025), a confidence-driven compression and assessment framework for 3D Gaussian Splatting (3DGS), and "ConfidentSplat: Confidence-Weighted Depth Fusion for Accurate 3D Gaussian Splatting SLAM" (Dufera et al., 21 Sep 2025), an RGB-only dense SLAM system built on 3DGS. The former treats confidence as a learnable per-splat importance variable for pruning and scene assessment; the latter treats confidence as a pixel-wise reliability estimate for fusing multi-view geometric depth with monocular priors.

1. Terminological scope and conceptual distinction

The two uses of the name occupy different layers of the Gaussian Splatting pipeline. One is a lossy compression method that leaves the base splatting representation essentially unchanged and learns which splats matter. The other is a SLAM system in which confidence-weighted proxy depth guides map optimization.

Name	Title	Primary role
Confident Splatting	"Confident Splatting: Confidence-Based Compression of 3D Gaussian Splatting via Learnable Beta Distributions"	confidence-driven compression and assessment
ConfidentSplat	"ConfidentSplat: Confidence-Weighted Depth Fusion for Accurate 3D Gaussian Splatting SLAM"	confidence-weighted depth fusion for RGB-only 3DGS SLAM

This distinction is essential because the two methods solve different problems. "Confident Splatting" addresses the fact that 3DGS often produces millions of splats, resulting in excessive storage and computational overhead, and frames the problem as a lossy compression problem rather than representation rewriting. "ConfidentSplat" addresses geometric inaccuracies in existing RGB-only 3DGS SLAM methods that stem from unreliable depth estimation and introduces a confidence-weighted fusion mechanism for proxy depth generation (Razlighi et al., 28 Jun 2025, Dufera et al., 21 Sep 2025).

2. Confidence as a learnable per-splat variable

In "Confident Splatting," each splat receives a learnable confidence variable on top of an otherwise unchanged splatting pipeline. The central design choice is a probabilistic parameterization: $C_i \sim \mathrm{Beta}(\alpha_i,\beta_i),$ with density

$$f_X(x;\alpha,\beta)=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}x^{\alpha-1}(1-x)^{\beta-1} =\frac{1}{B(\alpha,\beta)}x^{\alpha-1}(1-x)^{\beta-1}.$$

The operative confidence score is the expected value

$$c_i = \mathbb{E}[C_i] = \frac{\alpha_i}{\alpha_i+\beta_i}.$$

The method emphasizes that it does not directly regress a scalar confidence; instead, the Beta parameterization provides a distributional model, letting the method reason about uncertainty and confidence shape. In practice, the parameters are passed through a positivity-enforcing transform inside the entropy term via $\mathrm{Softplus}(\cdot)$, and the total objective combines the base reconstruction loss with three confidence regularizers: $$\mathcal{L}_{total}=\mathcal{L}_{rec}+\lambda_1\mathcal{L}_{sparse}+\lambda_2\mathcal{L}_{ent}+\lambda_3\mathcal{L}_{sal}.$$ The sparsity term is

$\mathcal{L}_{sparse}=\frac{1}{N}\sum_{i=1}^N c_i.$

The entropy regularizer prevents all confidences from collapsing to $0$ or $1$ by penalizing low-entropy Beta distributions. The saliency term is a ranking-based hinge loss over pairs of splats sampled according to image-space gradient saliency; if $i$ is more salient than $j$, the method encourages $$f_X(x;\alpha,\beta)=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}x^{\alpha-1}(1-x)^{\beta-1} =\frac{1}{B(\alpha,\beta)}x^{\alpha-1}(1-x)^{\beta-1}.$$0. This makes confidence a relative quantity tied to reconstruction gradients rather than a purely absolute score. The same framework is architecture-agnostic because it only adds per-splat confidence parameters and a slight opacity modulation, and it can be attached to vanilla 3DGS and to variants such as MCMC-GS with minimal modification (Razlighi et al., 28 Jun 2025).

3. Rendering integration, pruning, and compression behavior

Confidence is injected into rendering by modulating opacity before rasterization. If $$f_X(x;\alpha,\beta)=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}x^{\alpha-1}(1-x)^{\beta-1} =\frac{1}{B(\alpha,\beta)}x^{\alpha-1}(1-x)^{\beta-1}.$$1 is the original opacity, the effective opacity is written as the original opacity passed through $$f_X(x;\alpha,\beta)=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}x^{\alpha-1}(1-x)^{\beta-1} =\frac{1}{B(\alpha,\beta)}x^{\alpha-1}(1-x)^{\beta-1}.$$2 and multiplied by $$f_X(x;\alpha,\beta)=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}x^{\alpha-1}(1-x)^{\beta-1} =\frac{1}{B(\alpha,\beta)}x^{\alpha-1}(1-x)^{\beta-1}.$$3, so confident splats contribute more strongly to the rendered image and low-confidence splats contribute less. This makes confidence a train-time rendering signal rather than merely a post-hoc score.

Compression is deliberately postponed until after training. The method does not remove splats during optimization; instead, after training it performs test-time pruning by thresholding confidence: $$f_X(x;\alpha,\beta)=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}x^{\alpha-1}(1-x)^{\beta-1} =\frac{1}{B(\alpha,\beta)}x^{\alpha-1}(1-x)^{\beta-1}.$$4 By sweeping $$f_X(x;\alpha,\beta)=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}x^{\alpha-1}(1-x)^{\beta-1} =\frac{1}{B(\alpha,\beta)}x^{\alpha-1}(1-x)^{\beta-1}.$$5, the user can choose any desired quality-size tradeoff without retraining. This is presented as a practical distinction from train-time pruning methods such as RadSplat or LP-3DGS, which require retraining if the pruning ratio changes. The paper also notes that the method can optionally be used to identify the “main object” or remove floaters by raising the confidence threshold, since low-confidence background and artifact splats tend to disappear first (Razlighi et al., 28 Jun 2025).

The reported empirical pattern is that confidence-thresholding behaves smoothly: as the threshold increases, splat count drops dramatically while PSNR, SSIM, and LPIPS degrade slowly. On the Garden scene, more than half the splats can be pruned with less than about $$f_X(x;\alpha,\beta)=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}x^{\alpha-1}(1-x)^{\beta-1} =\frac{1}{B(\alpha,\beta)}x^{\alpha-1}(1-x)^{\beta-1}.$$6 dB PSNR drop. On Flowers, the baseline 3DGS has $$f_X(x;\alpha,\beta)=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}x^{\alpha-1}(1-x)^{\beta-1} =\frac{1}{B(\alpha,\beta)}x^{\alpha-1}(1-x)^{\beta-1}.$$7M Gaussians and PSNR $$f_X(x;\alpha,\beta)=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}x^{\alpha-1}(1-x)^{\beta-1} =\frac{1}{B(\alpha,\beta)}x^{\alpha-1}(1-x)^{\beta-1}.$$8, whereas Ours@Base3DGS at about $$f_X(x;\alpha,\beta)=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}x^{\alpha-1}(1-x)^{\beta-1} =\frac{1}{B(\alpha,\beta)}x^{\alpha-1}(1-x)^{\beta-1}.$$9 keeps $$c_i = \mathbb{E}[C_i] = \frac{\alpha_i}{\alpha_i+\beta_i}.$$0M Gaussians with essentially unchanged PSNR ($$c_i = \mathbb{E}[C_i] = \frac{\alpha_i}{\alpha_i+\beta_i}.$$1). On Eiffel Tower, Ours@Base3DGS at about $$c_i = \mathbb{E}[C_i] = \frac{\alpha_i}{\alpha_i+\beta_i}.$$2 pruning retains $$c_i = \mathbb{E}[C_i] = \frac{\alpha_i}{\alpha_i+\beta_i}.$$3 Gaussians with PSNR $$c_i = \mathbb{E}[C_i] = \frac{\alpha_i}{\alpha_i+\beta_i}.$$4, compared to $$c_i = \mathbb{E}[C_i] = \frac{\alpha_i}{\alpha_i+\beta_i}.$$5 at full confidence and $$c_i = \mathbb{E}[C_i] = \frac{\alpha_i}{\alpha_i+\beta_i}.$$6 for vanilla 3DGS. On the MCMC-GS variant, for Eiffel Tower, PSNR remains $$c_i = \mathbb{E}[C_i] = \frac{\alpha_i}{\alpha_i+\beta_i}.$$7 at both original and $$c_i = \mathbb{E}[C_i] = \frac{\alpha_i}{\alpha_i+\beta_i}.$$8 retained settings, and $$c_i = \mathbb{E}[C_i] = \frac{\alpha_i}{\alpha_i+\beta_i}.$$9 at $\mathrm{Softplus}(\cdot)$0 (Razlighi et al., 28 Jun 2025).

4. Scene assessment, metrics, and limitations

Beyond compression, "Confident Splatting" proposes a scene-level quality metric based on average confidence: $\mathrm{Softplus}(\cdot)$1 This “Average Confidence Score” is interpreted as a proxy for scene quality: scenes with fewer floaters, fewer artifacts, and generally better reconstruction quality tend to have higher ACS. In the experiments, higher-PSNR, cleaner scenes typically show higher ACS, suggesting the score can be used for comparison across scenes or to guide automated refinement.

The evaluation spans BILARF test scenes (\textit{building, chinesearch, lionpavilion, nighttimepond, pondbike, statue, strat}), MipNeRF-360 (\textit{garden}, plus extra \textit{flowers} and \textit{treehill}), a ZipNeRF \textit{berlin} scene, Tanks and Temples (\textit{truck}, \textit{train}), and custom “in-the-wild” YouTube/SfM scenes such as the Eiffel Tower, Big Ben, Louvre Museum, and Persepolis. Metrics include PSNR, SSIM, LPIPS, number of Gaussians, and the proposed SQR metric. Lower SQR is better, since it rewards fewer splats for a given PSNR, and the scale term is chosen on the same order as the original scene size, typically $\mathrm{Softplus}(\cdot)$2–$\mathrm{Softplus}(\cdot)$3, so that compression and fidelity are balanced (Razlighi et al., 28 Jun 2025).

The limitations are explicit. The method is not a lossless compression method; it trades fidelity for size and assumes that some quality degradation is acceptable. The threshold still needs to be selected manually based on the target tradeoff. The saliency loss adds computation because it relies on sampling ranked splat pairs from image-space gradients. The method also does not fundamentally solve the original problem of producing fewer splats during training; it learns a better importance signal for pruning. The Gumbel-noise variants that were tested, both multiplicatively and additively with temperature $\mathrm{Softplus}(\cdot)$4, did not outperform the simpler raw confidence $\mathrm{Softplus}(\cdot)$5, so the final method does not use Gumbel noise (Razlighi et al., 28 Jun 2025).

5. ConfidentSplat as confidence-weighted depth fusion for 3DGS SLAM

"ConfidentSplat" in the SLAM literature is a different system. It is an RGB-only dense SLAM pipeline built on 3D Gaussian Splatting that follows a structure with a tracking frontend, a mapping backend, a global optimization layer for loop closure and bundle adjustment, and a depth fusion module that creates proxy depth used to supervise the map. Its central claim is that RGB-only 3DGS SLAM methods often produce good rendering but inaccurate geometry because the depth supervision used to build the map is unreliable, especially when multi-view geometry is sparse or noisy and monocular priors are dense but not metrically stable (Dufera et al., 21 Sep 2025).

The core fusion rule for keyframe $\mathrm{Softplus}(\cdot)$6 and pixel $\mathrm{Softplus}(\cdot)$7 is

$\mathrm{Softplus}(\cdot)$8

with

$\mathrm{Softplus}(\cdot)$9

The main reliability signal for multi-view depth is a geometric consistency count

$$\mathcal{L}_{total}=\mathcal{L}_{rec}+\lambda_1\mathcal{L}_{sparse}+\lambda_2\mathcal{L}_{ent}+\lambda_3\mathcal{L}_{sal}.$$0

which is normalized as

$$\mathcal{L}_{total}=\mathcal{L}_{rec}+\lambda_1\mathcal{L}_{sparse}+\lambda_2\mathcal{L}_{ent}+\lambda_3\mathcal{L}_{sal}.$$1

The aligned monocular inverse depth is

$$\mathcal{L}_{total}=\mathcal{L}_{rec}+\lambda_1\mathcal{L}_{sparse}+\lambda_2\mathcal{L}_{ent}+\lambda_3\mathcal{L}_{sal}.$$2

The tracking frontend uses a DROID-SLAM-inspired Disparity, Scale, and Pose Optimization layer that jointly optimizes camera poses $$\mathcal{L}_{total}=\mathcal{L}_{rec}+\lambda_1\mathcal{L}_{sparse}+\lambda_2\mathcal{L}_{ent}+\lambda_3\mathcal{L}_{sal}.$$3, selected high-error inverse depths $$\mathcal{L}_{total}=\mathcal{L}_{rec}+\lambda_1\mathcal{L}_{sparse}+\lambda_2\mathcal{L}_{ent}+\lambda_3\mathcal{L}_{sal}.$$4, scale $$\mathcal{L}_{total}=\mathcal{L}_{rec}+\lambda_1\mathcal{L}_{sparse}+\lambda_2\mathcal{L}_{ent}+\lambda_3\mathcal{L}_{sal}.$$5, and shift $$\mathcal{L}_{total}=\mathcal{L}_{rec}+\lambda_1\mathcal{L}_{sparse}+\lambda_2\mathcal{L}_{ent}+\lambda_3\mathcal{L}_{sal}.$$6. The map is a deformable 3DGS representation with Gaussian mean $$\mathcal{L}_{total}=\mathcal{L}_{rec}+\lambda_1\mathcal{L}_{sparse}+\lambda_2\mathcal{L}_{ent}+\lambda_3\mathcal{L}_{sal}.$$7, covariance $$\mathcal{L}_{total}=\mathcal{L}_{rec}+\lambda_1\mathcal{L}_{sparse}+\lambda_2\mathcal{L}_{ent}+\lambda_3\mathcal{L}_{sal}.$$8, opacity $$\mathcal{L}_{total}=\mathcal{L}_{rec}+\lambda_1\mathcal{L}_{sparse}+\lambda_2\mathcal{L}_{ent}+\lambda_3\mathcal{L}_{sal}.$$9, and spherical harmonics coefficients for color, with covariance parameterized as

$\mathcal{L}_{sparse}=\frac{1}{N}\sum_{i=1}^N c_i.$0

Map optimization combines photometric supervision using $\mathcal{L}_{sparse}=\frac{1}{N}\sum_{i=1}^N c_i.$1 and SSIM, depth supervision aligning rendered depth with fused proxy depth, and covariance regularization. The implementation is in Python + PyTorch, built on Splat-SLAM, and uses DROID-SLAM-inspired tracking, RAFT optical flow, Omnidata monocular depth priors, and a 3D Gaussian Splatting mapper on an NVIDIA A100 GPU. Reported hyperparameters include $\mathcal{L}_{sparse}=\frac{1}{N}\sum_{i=1}^N c_i.$2, $\mathcal{L}_{sparse}=\frac{1}{N}\sum_{i=1}^N c_i.$3, $\mathcal{L}_{sparse}=\frac{1}{N}\sum_{i=1}^N c_i.$4, and $\mathcal{L}_{sparse}=\frac{1}{N}\sum_{i=1}^N c_i.$5 (Dufera et al., 21 Sep 2025).

The method is evaluated on TUM RGB-D, ScanNet v2, Replica, and a custom mobile dataset. Selected TUM-RGBD tracking results are $\mathcal{L}_{sparse}=\frac{1}{N}\sum_{i=1}^N c_i.$6 cm ATE RMSE on fr1/desk, $\mathcal{L}_{sparse}=\frac{1}{N}\sum_{i=1}^N c_i.$7 cm on fr2/xyz, $\mathcal{L}_{sparse}=\frac{1}{N}\sum_{i=1}^N c_i.$8 cm on fr3/office, and an average reported $\mathcal{L}_{sparse}=\frac{1}{N}\sum_{i=1}^N c_i.$9 cm. On ScanNet, average view-synthesis results are PSNR $0$0, SSIM $0$1, and LPIPS $0$2. On Replica, the reported geometry metrics are Depth L1 $0$3, Accuracy $0$4 cm, Completion $0$5 cm, and Completion ratio $0$6. On the custom phone-captured dataset, compared with Splat-SLAM, RMSE improves from $0$7 to $0$8, PSNR from $0$9 to $1$0, SSIM from $1$1 to $1$2, LPIPS from $1$3 to $1$4, and overall depth L1 from $1$5 to $1$6 (Dufera et al., 21 Sep 2025).

6. Relation to uncertainty-aware splatting research

The broader literature shows that “confidence-aware splatting” is not a single technique but a family of probabilistic or uncertainty-sensitive augmentations. GraphiXS turns 4D Gaussian Splatting into a graphical model over camera pose $1$7, frame time $1$8, image pixels $1$9, and latent component set $i$0, uses MAP estimation with SGHMC, and introduces a component-confidence term

$i$1

which measures whether a component is consistently visible and useful across the dataset. It is explicitly presented as broader than a confidence map on splats because it also models camera/time uncertainty, motion uncertainty, and priors on splat parameters, and it can instantiate Gaussian splats as GraphiGS or Student’s-$i$2 splats as GraphiTS (Yilmaz et al., 27 Jan 2026).

A different line of work addresses uncertainty estimation for novel views in Gaussian Splatting from primitive-based representations of error and visibility. That method projects training-view error and visibility onto Gaussian primitives, renders FoV counter, visibility, and error feature maps for novel views, and learns a pixel-wise regressor on holdout data. Its reported uncertainty estimates show high correlations to true errors, outperforming state-of-the-art methods, especially on foreground objects, and the trained regression models show generalization capabilities to new scenes (Gottwald et al., 4 Aug 2025).

These comparisons clarify a recurrent misconception. In one case, confidence is the expected value of a learnable Beta distribution attached to each splat and used for pruning. In another, confidence is a pixel-wise weight for fusing multi-view depth with monocular priors in a SLAM system. In GraphiXS, confidence is one factor in a graphical-model-based probabilistic generalization of 4D Gaussian Splatting. In primitive-based uncertainty estimation, confidence is derived from training-view visibility and error contribution and then aggregated by regression. A plausible implication is that “confidence” in Gaussian Splatting is best understood as a task-specific reliability variable rather than a single canonical quantity.