DreamSat-Pose: Spacecraft Pose Estimation from Single-View 3D Reconstructions and Learned 2D-3D Feature Matching

arXiv:2607.13449 · cs.CV, cs.LG · Submitted 2026-07-15 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-07-15

Updated: 2026-09-24

Project page: https://dreamsatpose.github.io

Terminology

• Transformer — 12× in this paper · explained in 2 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Alignment — 7× in this paper · explained in 1 episode
• Fidelity — 7× in this paper · explained in 2 episodes
• Generalization — 7× in this paper · explained in 13 episodes
• Feature Extraction — 6× in this paper · explained in 2 episodes
• Point Clouds — 5× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• 6D Object Pose Estimation — 2× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Consistency — 2× in this paper · explained in 3 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Zero-Shot Generalization — 2× in this paper · explained in 5 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Chamfer Distance — 1× in this paper
• Classical Perspective-n-Point — 1× in this paper
• Closed-Loop Control — 1× in this paper · explained in 3 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Hausdorff Distance — 1× in this paper
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Segment Anything Model (SAM) — 1× in this paper · explained in 2 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• DINOv3
• SuperGlue: Learning Feature Matching with Graph Neural Networks
• Survival of heavy flavored mesons in a hot medium
• Segment Anything

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models