Dropping Anchor and Spherical Harmonics for Sparse-view Gaussian Splatting

• URL: http://arxiv.org/abs/2602.20933v1
• Date: Tue, 24 Feb 2026 14:11:56 GMT
• Title: Dropping Anchor and Spherical Harmonics for Sparse-view Gaussian Splatting
• Abstract summary: We propose DropAnSH-GS, a novel anchor-based Dropout strategy.<n>Our method randomly selects certain Gaussians as anchors and simultaneously removes their spatial neighbors.<n>We extend the Dropout to color attributes by randomly dropping higher-degree SH to concentrate appearance information in lower-degree SH.
• Score: 31.833331215016475
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent 3D Gaussian Splatting (3DGS) Dropout methods address overfitting under sparse-view conditions by randomly nullifying Gaussian opacities. However, we identify a neighbor compensation effect in these approaches: dropped Gaussians are often compensated by their neighbors, weakening the intended regularization. Moreover, these methods overlook the contribution of high-degree spherical harmonic coefficients (SH) to overfitting. To address these issues, we propose DropAnSH-GS, a novel anchor-based Dropout strategy. Rather than dropping Gaussians independently, our method randomly selects certain Gaussians as anchors and simultaneously removes their spatial neighbors. This effectively disrupts local redundancies near anchors and encourages the model to learn more robust, globally informed representations. Furthermore, we extend the Dropout to color attributes by randomly dropping higher-degree SH to concentrate appearance information in lower-degree SH. This strategy further mitigates overfitting and enables flexible post-training model compression via SH truncation. Experimental results demonstrate that DropAnSH-GS substantially outperforms existing Dropout methods with negligible computational overhead, and can be readily integrated into various 3DGS variants to enhance their performances. Project Website: https://sk-fun.fun/DropAnSH-GS

Related papers

• PairDropGS: Paired Dropout-Induced Consistency Regularization for Sparse-View Gaussian Splatting [81.95709983545666]
  Dropout-based sparse-view 3DGS methods alleviate overfitting by randomly suppressing Gaussian primitives during training.<n>We propose PairDropGS, a Paired Dropout-induced Consistency Regularization framework for sparse-view Gaussian splatting.<n>Experiments on widely-used sparse-view benchmarks demonstrate that PairDropGS achieves superior training stability, significantly outperforms existing dropout-based 3DGS methods in reconstruction quality.
  arXiv  Detail & Related papers  (2026-05-12T13:00:39Z)
• GS^2: Graph-based Spatial Distribution Optimization for Compact 3D Gaussian Splatting [50.2045409672451]
  3D Gaussian Splatting (3DGS) has demonstrated breakthrough performance in novel view synthesis and real-time rendering.<n>It is constrained by the high memory cost due to a huge number of Gaussian points.<n>We propose graph-based spatial distribution optimization for compact 3DGS.
  arXiv  Detail & Related papers  (2026-04-02T10:41:51Z)
• F4Splat: Feed-Forward Predictive Densification for Feed-Forward 3D Gaussian Splatting [28.800888081472422]
  We present F4Splat, which performs Feed-Forward predictive densification for Feed-Forward 3D Gaussian Splatting.<n>Our model predicts per-region densification scores to estimate the required Gaussian density and allows explicit control over the final Gaussian budget.<n>Experiments demonstrate that our model achieves superior novel-view synthesis performance compared to prior uncalibrated feed-forward methods.
  arXiv  Detail & Related papers  (2026-03-22T16:03:56Z)
• D$^2$GS: Depth-and-Density Guided Gaussian Splatting for Stable and Accurate Sparse-View Reconstruction [73.61056394880733]
  3D Gaussian Splatting (3DGS) enables real-time, high-fidelity novel view synthesis (NVS) with explicit 3D representations.<n>We identify two key failure modes under sparse-view conditions: overfitting in regions with excessive Gaussian density near the camera, and underfitting in distant areas with insufficient Gaussian coverage.<n>We propose a unified framework D$2$GS, comprising two key components: a Depth-and-Density Guided Dropout strategy, and a Distance-Aware Fidelity Enhancement module.
  arXiv  Detail & Related papers  (2025-10-09T17:59:49Z)
• UGOD: Uncertainty-Guided Differentiable Opacity and Soft Dropout for Enhanced Sparse-View 3DGS [8.78995910690481]
  3D Gaussian Splatting (3DGS) has become a competitive approach for novel view synthesis (NVS)<n>We investigate how adaptive weighting of Gaussians affects rendering quality, which is characterised by learned uncertainties proposed.<n>Our method achieves 3.27% PSNR improvements on the MipNeRF 360 dataset.
  arXiv  Detail & Related papers  (2025-08-07T01:42:22Z)
• Metropolis-Hastings Sampling for 3D Gaussian Reconstruction [31.840492077537018]
  We propose an adaptive sampling framework for 3D Gaussian Splatting (3DGS)<n>Our framework overcomes limitations by reformulating densification and pruning as a probabilistic sampling process.<n>Our approach achieves faster convergence while matching or modestly surpassing the view-synthesis quality of state-of-the-art models.
  arXiv  Detail & Related papers  (2025-06-15T19:12:37Z)
• Steepest Descent Density Control for Compact 3D Gaussian Splatting [72.54055499344052]
  3D Gaussian Splatting (3DGS) has emerged as a powerful real-time, high-resolution novel view.<n>We propose a theoretical framework that demystifies and improves density control in 3DGS.<n>We introduce SteepGS, incorporating steepest density control, a principled strategy that minimizes loss while maintaining a compact point cloud.
  arXiv  Detail & Related papers  (2025-05-08T18:41:38Z)
• DropoutGS: Dropping Out Gaussians for Better Sparse-view Rendering [45.785618745095164]
  3D Gaussian Splatting (3DGS) has demonstrated promising results in novel view synthesis.<n>As the number of training views decreases, the novel view synthesis task degrades to a highly under-determined problem.<n>We propose a Random Dropout Regularization (RDR) to exploit the advantages of low-complexity models to alleviate overfitting.<n>In addition, to remedy the lack of high-frequency details for these models, an Edge-guided Splitting Strategy (ESS) is developed.
  arXiv  Detail & Related papers  (2025-04-13T09:17:21Z)
• Pushing Rendering Boundaries: Hard Gaussian Splatting [72.28941128988292]
  3D Gaussian Splatting (3DGS) has demonstrated impressive Novel View Synthesis (NVS) results in a real-time rendering manner.<n>We propose Hard Gaussian Splatting, dubbed HGS, which considers multi-view significant positional gradients and rendering errors to grow hard Gaussians.<n>Our method achieves state-of-the-art rendering quality while maintaining real-time efficiency.
  arXiv  Detail & Related papers  (2024-12-06T07:42:47Z)
• GaussianSpa: An "Optimizing-Sparsifying" Simplification Framework for Compact and High-Quality 3D Gaussian Splatting [12.342660713851227]
  3D Gaussian Splatting (3DGS) has emerged as a mainstream for novel view synthesis, leveraging continuous aggregations of Gaussian functions.<n>3DGS suffers from substantial memory requirements to store the multitude of Gaussians, hindering its practicality.<n>We introduce GaussianSpa, an optimization-based simplification framework for compact and high-quality 3DGS.
  arXiv  Detail & Related papers  (2024-11-09T00:38:06Z)
• Spiking GS: Towards High-Accuracy and Low-Cost Surface Reconstruction via Spiking Neuron-based Gaussian Splatting [41.33048562081358]
  3D Gaussian Splatting is capable of reconstructing 3D scenes in minutes.<n>Despite advances in surface reconstruction accuracy, the reconstructed results still exhibit bias and suffer from inefficiency in storage and training.<n>This paper provides a different observation on the cause of the inefficiency and the reconstruction bias, which is attributed to the integration of the low-opacity parts.
  arXiv  Detail & Related papers  (2024-10-09T01:39:26Z)
• Mipmap-GS: Let Gaussians Deform with Scale-specific Mipmap for Anti-aliasing Rendering [81.88246351984908]
  We propose a unified optimization method to make Gaussians adaptive for arbitrary scales. Inspired by the mipmap technique, we design pseudo ground-truth for the target scale and propose a scale-consistency guidance loss to inject scale information into 3D Gaussians. Our method outperforms 3DGS in PSNR by an average of 9.25 dB for zoom-in and 10.40 dB for zoom-out.
  arXiv  Detail & Related papers  (2024-08-12T16:49:22Z)
• HAC: Hash-grid Assisted Context for 3D Gaussian Splatting Compression [55.6351304553003]
  3D Gaussian Splatting (3DGS) has emerged as a promising framework for novel view synthesis. We propose a Hash-grid Assisted Context (HAC) framework for highly compact 3DGS representation. Our work is the pioneer to explore context-based compression for 3DGS representation, resulting in a remarkable size reduction of over $75times$ compared to vanilla 3DGS.
  arXiv  Detail & Related papers  (2024-03-21T16:28:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.