LEGAU: Learning Semantic Gaussian Priors for Scalable Category-level Pose Estimation

arXiv:2609.35046 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Embeddings — 21× in this paper · explained in 2 episodes
• Transformer — 21× in this paper · explained in 2 episodes
• Alignment — 16× in this paper · explained in 1 episode
• Accuracy — 8× in this paper · explained in 1 episode
• Consistency — 8× in this paper · explained in 3 episodes
• 6D Object Pose Estimation — 7× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 7× in this paper
• Gaussian Splatting — 7× in this paper · explained in 4 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• AUC — 5× in this paper · explained in 2 episodes
• 3D Gaussian Splatting — 4× in this paper · explained in 2 episodes
• Unified Framework — 4× in this paper · explained in 5 episodes
• Ablation — 3× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 3× in this paper
• European Conference on Computer Vision — 3× in this paper
• Fidelity — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Point Clouds — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Normalized Object Coordinate Space — 2× in this paper
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• alpha — 2× in this paper · explained in 1 episode
• 3D Gaussian Splatting (3D-GS) — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Multi-objective Optimization — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Volume Under Surface — 1× in this paper

Sources

• One View, Many Worlds: Single-Image to 3D Object Meets Generative Domain Randomization for One-Shot 6D Pose Estimation
• GCE-Pose: Global Context Enhancement for Category-level Object Pose Estimation
• SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation
• GigaPose: Fast and Robust Novel Object Pose Estimation via One Correspondence
• DINOv2: Learning Robust Visual Features without Supervision
• Amodal3R: Amodal 3D Reconstruction from Occluded 2D Images
• AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers
• Beyond 'Templates': Category-Agnostic Object Pose, Size, and Shape Estimation from a Single View
• GenPose: Generative Category-level Object Pose Estimation via Diffusion Models

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