# CAdam: Context-Adaptive Moment Estimation for 3D Gaussian Densification in Generative Distillation

arXiv: https://arxiv.org/abs/2605.20872 · HTML: https://mlsanity.com/paper/2605.20872/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 47 of its 61 references that are in the map.
- First posted on arXiv 20 May 2026.

## Builds on (most cited first, 30 of 47)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023)
- [ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation](https://mlsanity.com/paper/2304.05977/) (2023)
- [Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis](https://mlsanity.com/paper/2306.09341/) (2023)
- [Structured 3D Latents for Scalable and Versatile 3D Generation](https://mlsanity.com/paper/2412.01506/) (2024)
- [LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation](https://mlsanity.com/paper/2402.05054/) (2024)
- [GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation](https://mlsanity.com/paper/2403.14621/) (2024)
- [Text-to-3D using Gaussian Splatting](https://mlsanity.com/paper/2309.16585/) (2023)
- [LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching](https://mlsanity.com/paper/2311.11284/) (2023)
- [3D Gaussian Splatting as Markov Chain Monte Carlo](https://mlsanity.com/paper/2404.09591/) (2024)
- [Text-to-3D with Classifier Score Distillation](https://mlsanity.com/paper/2310.19415/) (2023)
- [Consistent3D: Towards Consistent High-Fidelity Text-to-3D Generation with Deterministic Sampling Prior](https://mlsanity.com/paper/2401.09050/) (2024)
- [GVGEN: Text-to-3D Generation with Volumetric Representation](https://mlsanity.com/paper/2403.12957/) (2024)
- [Score Distillation via Reparametrized DDIM](https://mlsanity.com/paper/2405.15891/) (2024)
- [Cycle3D: High-quality and Consistent Image-to-3D Generation via Generation-Reconstruction Cycle](https://mlsanity.com/paper/2407.19548/) (2024)
- [Trim 3D Gaussian Splatting for Accurate Geometry Representation](https://mlsanity.com/paper/2406.07499/) (2024)
- [Score Distillation Sampling with Learned Manifold Corrective](https://mlsanity.com/paper/2401.05293/) (2024)
- [Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting](https://mlsanity.com/paper/2403.09981/) (2024)
- [Hyper-3DG: Text-to-3D Gaussian Generation via Hypergraph](https://mlsanity.com/paper/2403.09236/) (2024)
- [EDGS: Eliminating Densification for Efficient Convergence of 3DGS](https://mlsanity.com/paper/2504.13204/) (2025)
- [UniLat3D: Geometry-Appearance Unified Latents for Single-Stage 3D Generation](https://mlsanity.com/paper/2509.25079/) (2025)
- [Connecting Consistency Distillation to Score Distillation for Text-to-3D Generation](https://mlsanity.com/paper/2407.13584/) (2024)
- [Consistent Flow Distillation for Text-to-3D Generation](https://mlsanity.com/paper/2501.05445/) (2025)
- [VividDreamer: Invariant Score Distillation For Hyper-Realistic Text-to-3D Generation](https://mlsanity.com/paper/2407.09822/) (2024)
- [Turbo3D: Ultra-fast Text-to-3D Generation](https://mlsanity.com/paper/2412.04470/) (2024)
- [Dreamer XL: Towards High-Resolution Text-to-3D Generation via Trajectory Score Matching](https://mlsanity.com/paper/2405.11252/) (2024)

## Abstract (arXiv)

Adaptive densification is the engine of 3D Gaussian Splatting (3DGS). However, when transposed to the optimization-based Generative Distillation paradigm, this reconstruction-native mechanism reveals fundamental limitations, resulting in inefficient representations cluttered with redundant primitives. We diagnose this failure as a Densification Dilemma stemming from the stochastic nature of generative guidance: the standard magnitude-based accumulation indiscriminately aggregates transient noise alongside geometric signals, making it difficult to strike a balance between over-densification and under-fitting. To resolve this, we introduce Context-Adaptive Moment Estimation (CAdam), a novel framework that reinterprets densification as a statistically grounded signal verification problem. CAdam leverages the first moment of gradients to exploit the interference principle, where stochastic fluctuations cancel out via destructive interference while consistent geometric drifts accumulate via constructive interference, effectively disentangling the underlying signal from the generative noise floor. This is further augmented by a quantile-based context awareness and an intrinsic Signal-to-Noise Ratio (SNR) gating mechanism, which ensure robust adaptation across optimization stages and enable the soft termination of densification. Extensive experiments across diverse objectives (SDS, ISM, VFDS) and strong generative 3DGS backbones show that CAdam reduces Gaussian count by 85%-97% relative to standard densification while preserving overall comparable perceptual quality. These results highlight signal-aware density control as a practical way to improve memory efficiency in optimization-based generative distillation.