GAPS: Generative Active Pseudo-view Selection for Sparse-View 3D Gaussian Splatting

arXiv:2609.23436 · cs.CV · Submitted 2026-09-20 · Read on arXiv

cs.CV

Submitted: 2026-09-20

Updated: 2026-09-24

Terminology

• Diffusion Model — 28× in this paper · explained in 8 episodes
• Gaussian Splatting — 21× in this paper · explained in 4 episodes
• Regularization — 19× in this paper · explained in 3 episodes
• Diffusion Models — 12× in this paper · explained in 6 episodes
• 3D Gaussian Splatting — 10× in this paper · explained in 2 episodes
• alpha — 10× in this paper · explained in 1 episode
• Ablation — 9× in this paper · explained in 1 episode
• Generative Active Pseudo-view Selection — 8× in this paper
• Adaptation — 7× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 5× in this paper
• Consistency — 5× in this paper · explained in 3 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 4× in this paper
• Diffusion Process — 4× in this paper · explained in 2 episodes
• Failure Modes — 4× in this paper · explained in 3 episodes
• Latent Diffusion Model — 4× in this paper · explained in 2 episodes
• Latent Diffusion Models — 4× in this paper · explained in 2 episodes
• 3D Gaussian Splatting (3DGS) — 3× in this paper · explained in 4 episodes
• European Conference on Computer Vision — 3× in this paper
• Accuracy — 2× in this paper · explained in 1 episode
• Distillation — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• LoRA Fine-Tuning — 2× in this paper · explained in 3 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptivity — 1× in this paper · explained in 1 episode
• Alternation — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Fisher Information — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Information Gain — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models (LDMs) — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Structurefrom-Motion and Multi-View Stereo — 1× in this paper
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Twelfth International Conference on Learning Representations — 1× in this paper
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes

Sources

• OracleGS: Grounding Generative Priors for Sparse-View Gaussian Splatting
• IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
• Frequency-based View Selection in Gaussian Splatting Reconstruction
• Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis

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