Gaussian Splatting Conditioning and an Analytical Framework

Introduction: Tooling for an explicit 3D representation

Reconstructing realistic 3D scenes from a few photographs is one of the most exciting frontiers in computer vision, yet gaining insight into how these representations are formed remains difficult. With Neural Radiance Fields (NeRFs), the research ecosystem benefited from rich tooling – live training previews, depth visualizations, and modular pipelines that accelerate iteration. By contrast, Gaussian Splatting, an explicit scene representation built from anisotropic 3D Gaussian primitives, enables real-time rendering but has lacked similarly mature, research-oriented analysis tools.

This gap complicates systematic comparisons between recent variants such as LightGaussian, Dynamic 3D Gaussians, LangSplat, and EndoGaussian – each introduces conditioning or compression strategies that change how Gaussians are initialized, optimized, and rendered. The project introduced here presents a modular, extensible framework that unifies multiple Gaussian Splatting variants under a common interface and couples them with a web-based viewer for interactive qualitative and quantitative analysis.

The goals are straightforward: reproducibility, rapid experimentation, and improved interpretability. To achieve these, the framework exposes a standardized command-line interface (CLI) for launching experiments and a live viewer that supports heatmaps, semantic search, checkpoint comparisons, and live training updates – all useful when comparing dense static scenes, dynamic sequences, or specialized medical reconstructions.

3D Gaussian Splatting

3D Gaussian Splatting represents a scene as a set of anisotropic Gaussians initialized from an SfM point cloud [2]. Each Gaussian is parameterized by a mean \(p\), a covariance (encoded as scale \(s\) and rotation quaternion \(q\)), an opacity \(\alpha\), and spherical harmonics \(SH\) coefficients for view-dependent color. Rendering is performed by splatting Gaussians into image space and performing alpha-blending over the ordered set \(N\) of Gaussians overlapping a pixel:

\[ C = \sum_{i\in N} c_i \alpha_i \prod_{j=1}^{i-1} (1 - \alpha_j). \tag{1} \]

Optimization minimizes a photo-metric objective combining L1 and D-SSIM:

\[ L = (1-\lambda)L_1 + \lambda L_{\text{D-SSIM}}. \tag{2} \]

Adaptive densification (adding Gaussians when positional gradients are large) and pruning (removing low-opacity Gaussians) are typical to control model complexity. Differentiable tile-based rasterizers make the rendering highly parallel and computationally efficient compared to earlier implicit methods.

Fig. 1: Overview of the 3D Gaussian Splatting pipeline.

LightGaussian

LightGaussian focuses on storage and runtime efficiency via principled pruning and feature compression [6]. A global significance score (combining opacity, volume and color contribution) enables safer pruning than simple opacity thresholds. SH distillation and vector quantization compress appearance features and codebook voxels, yielding large size reductions at the cost of slight accuracy loss.

Dynamic 3D Gaussians

Dynamic 3D Gaussians extend splatting to videos by fixing non-motion parameters and learning motion parameters (positions/rotations) per time step [7]. Physically motivated priors – short-term rigidity \(L_{\text{rigid}}\), local rotation similarity \(L_{\text{rot}}\), and long-term local isometry \(L_{\text{iso}}\) – encourage persistent, stable tracks for moving objects.

LangSplat

LangSplat introduces semantic conditioning by augmenting Gaussians with learned semantic features. Input images are segmented (e.g., via SAM), semantic features are extracted with CLIP, and a low-dimensional autoencoder compresses per-pixel semantics into a compact field [8]. During rendering semantic features are alpha-blended and guided by a CLIP-based similarity loss, enabling open-vocabulary queries over the 3D Gaussian field.

EndoGaussian

EndoGaussian targets endoscopic video reconstruction with binocular inputs [9]. Initialization uses stereo depth estimates to create an SfM-like point cloud. Rather than fixing motion-irrelevant parameters and learning simple motion, EndoGaussian predicts a deformation field with a neural network to enable spatio-temporal Gaussian tracking that better models tissue and instrument surface dynamics.

Methodology & Implementation

The framework was designed around two principles: ease of use and modularity. A unified CLI standardizes experiment launching across implementations and a web viewer (based on the open-source gsplat project) provides interactive exploration and analysis. The implementation strategy favors light integration effort: most third-party implementations are wrapped by small adapter classes and executed inside controlled Conda environments.

Framework architecture

A compact architecture centers on a Base class providing repository cloning and environment setup, plus two helpers – EnvironmentManager and RepoManager – for package and process control. Specific implementations inherit from Base; LightGaussian and the original implementation additionally inherit from GSBase to enable live viewer integration. This structure allows instantiating any implementation from the CLI with a consistent parameter interface.

Fig. 5: Framework dataflow – unified setup, environment management, and viewer integration.

Implementations supported

OriginalGaussianSplatting: wrapped with an OriginalGaussianSplatting class exposing common flags (fast rasterizer, use_depth, exposure compensation, use_viewer, resolution scaling).
LightGaussian: executed with standard LightGaussian settings and compression options.
Dynamic 3D Gaussians: training scripts adjusted to accept dataset-specific input/output paths.
LangSplat: implemented as a multi-stage pipeline (preprocess → train_autoencoder → train_3dgs → train_semantic_features) with an option to run all stages in sequence. The 3D GS backend can be selected (Original or LightGaussian).
EndoGaussian: Docker and environment tweaks were required (different rasterizer submodule, Open3D, imageio) and two canonical surgical scenarios (cutting, pulling) were exposed for training.

Fig. 6: Modular architecture enabling multiple Gaussian Splatting backends via a standardized CLI.

Viewer and analysis tools

A web viewer was adapted from the gsplat project and extended with analysis filters: Gaussian density, average opacity, average scale, and semantic feature visualization. The viewer supports model comparison (load two checkpoints and highlight parameter changes), live training updates, and semantic search via cosine similarity over LangSplat features.

Experiments

Datasets

Evaluation covered diverse benchmarks: Mip-NeRF 360 (high-resolution, full-scene captures) [11], Deep Blending (forward-facing indoor/outdoor) [12], Tanks and Temples (complex large-scale scenes) [13], plus temporal datasets used by Dynamic 3D Gaussians and EndoGaussian for dynamic and medical scenarios.

Visualization & interpretability

Qualitative tools include:

Heatmaps – Gaussian count per ray (density), average opacity, average scale.
Semantic visualization – highlight Gaussians by CLIP similarity to a text prompt (LangSplat).
Model comparison – highlight Gaussians with parameter changes in means, quaternions, scales, opacities, or colors between two checkpoints.
Live updates & densification plots – track number of Gaussians per training iteration to quantify growth and pruning.

These tools are integrated into the backend to preserve interactive rendering performance while providing research-grade diagnostics.

Results & Discussion

Two main categories of outcomes emerged: integration of multiple implementations for unified training and visualization / interpretability improvements. The framework preserves full functionality of the integrated projects while enabling systematic cross-implementation analysis.

Heatmaps and fine-scale inspection

Density, opacity, and scale heatmaps successfully expose internal structure of a model beyond rendered RGB. An option to clamp the maximum Gaussian scale improves observability of fine surface detail by suppressing large, scene-spanning Gaussians; this makes small surface features (e.g., table surface grain, tree bark)

Fig. 7: The selected heatmap visualizes the density of Gaussians. A reduced maximum scale (0.001) reveals fine Gaussian structures.

Objects near central camera poses tend to receive denser Gaussian coverage than peripheral regions, reflecting the influence of view distribution in training data.

Semantic conditioning (LangSplat)

Semantic feature visualization highlights objects whose CLIP features are distinctive (e.g., bicycle frames), enabling prompt-driven object highlighting inside the 3D Gaussian field. This suggests open-vocabulary querying over explicit Gaussian representations is feasible for well-defined object classes.

Fig. 8: Semantic similarity render mode (query: “bicycle”). The bicycle frame and a bottle are highlighted.

Checkpoint comparison and parameter evolution

The model-compare mode visualizes per-Gaussian parameter updates between two checkpoints (e.g., 15k vs 20k iterations). Means, opacities, and colors tend to receive larger updates, while quaternions and scales remain comparatively stable – an observation that aligns with the intuition that position and appearance refine more aggressively during late optimization. Indoor scenes generally required fewer Gaussians than outdoor scenes, where foliage and grass demand many small primitives.

Fig. 9: The compare render mode highlights Gaussians that received significant updates to their parameters. Thresholds set to 0.15.

Dynamics and temporal inspection

Dynamic scenes can be cached on the GPU for interactive playback. Although explicit Gaussian trajectory visualization is not implemented, average-scale heatmaps reveal motion patterns: foreground agents and moving objects show changing Gaussian locations across frames while backgrounds remain stable. Some artifacts – ghost Gaussians on the ground where moving people once stood – indicate imperfect trajectory learning for a subset of primitives.

Fig. 10: Two frames of a dynamic scene rendered with the average scale heatmap – moving humans and a ball are visible while background Gaussians remain static.

Final Thoughts

Limitations & Future Work

Several practical limitations remain. The viewer and backend currently depend on NVIDIA GPUs with compute capability of at least 7.0, which restricts portability. In terms of input formats, only PLY, NPZ, and gsplat PTH checkpoints are supported, and the lack of a unified community standard for Gaussian splats further complicates interoperability. Additionally, advanced trainer configuration remains primarily command-line driven, and providing richer configuration controls within the user interface would make the system more accessible to non-expert users.

Looking ahead, promising directions include enabling richer language–dynamic interactions – such as combining LangSplat with temporal conditioning – expanding support for additional formats, and incorporating explicit trajectory visualizations for dynamic scenes.

Conclusion

Explicit 3D Gaussian representations have advanced real-time, photorealistic rendering from sparse multi-view captures. While techniques such as LightGaussian focus on compression and representation efficiency, true conditioning strategies – for example semantic conditioning (LangSplat) or temporal/dynamic conditioning (Dynamic / EndoGaussian) – expand the applicability of Gaussian Splatting; however, meaningful comparison of these approaches requires dedicated tooling.

The presented framework combines a unified CLI, lightweight adapters for multiple implementations, and a feature-rich web viewer that surfaces internal Gaussian parameters through heatmaps, semantic search, model comparison, and live updates. By lowering the barrier to experimentation and enabling structured cross-method analysis, the framework aims to make Gaussian Splatting research more reproducible and interpretable – whether the target is large outdoor scenes, indoor reconstructions, dynamic sequences, or medical endoscopy.

List of Abbreviations

Abbreviation	Meaning
GS	Gaussian Splatting
SfM	Structure from Motion
SH	Spherical Harmonics
D-SSIM	Deep Structural Similarity
PLY	Polygon File Format
NPZ	NumPy compressed array
CLI	Command-Line Interface

References

[1] B. Mildenhall, P. P. Srinivasan, M. Tancik, et al., “NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis,” arXiv:2003.08934, 2020.

[2] B. Kerbl, G. Kopanas, T. Leimkuehler, and G. Drettakis, “3D Gaussian Splatting for Real-Time Radiance Field Rendering,” ACM Transactions on Graphics, vol. 42, no. 4, 2023.

[3] P. /. W. Eastcott, “Supersplat: Browser-based 3D Gaussian Splat Editor,” 2024.

[4] Polycam Team, “Polycam: Mobile and Web Capture with Gaussian Splatting,” 2024.

[5] F. Barthel et al., “Splatviz: Interactive Python-Based 3DGS Viewer,” 2024.

[6] Z. Fan, K. Wang, K. Wen, et al., “LightGaussian: Unbounded 3D Gaussian Compression with 15× Reduction and 200+ FPS,” arXiv:2311.17245, 2024.

[7] J. Luiten, G. Kopanas, B. Leibe, and D. Ramanan, “Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis,” arXiv:2308.09713, 2023.

[8] M. Qin, W. Li, J. Zhou, H. Wang, and H. Pfister, “LangSplat: 3D Language Gaussian Splatting,” arXiv:2312.16084, 2024.

[9] Y. Liu, C. Li, C. Yang, and Y. Yuan, “EndoGaussian: Real-Time Gaussian Splatting for Dynamic Endoscopic Scene Reconstruction,” arXiv:2401.12561, 2024.

[10] V. Ye, R. Li, J. Kerr, et al., “Gsplat: An Open-Source Library for Gaussian Splatting,” arXiv:2409.06765, 2024.

[11] J. T. Barron, B. Mildenhall, D. Verbin, et al., “Mip-NeRF 360,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

[12] P. Hedman, J. Philip, T. Price, et al., “Deep Blending for Free-Viewpoint Image-Based Rendering,” ACM Transactions on Graphics, 2018.

[13] A. Knapitsch, J. Park, Q.-Y. Zhou, and V. Koltun, “Tanks and Temples: Benchmarking Large-Scale Scene Reconstruction,” ACM Transactions on Graphics, 2017.