Monocular relative pose sensing is a central perception problem in non-cooperative rendezvous and on-orbit servicing. In spacecraft images, however, weak surface texture, thin appendages, illumination changes, and partial occlusion often leave only sparse and unstable geometric evidence.
arXiv:2607. 02360v1 Announce Type: cross Abstract: Monocular relative pose sensing is a central perception problem in non-cooperative rendezvous and on-orbit servicing.
arXiv:2607. 02360v3 Announce Type: replace-cross Abstract: Monocular spacecraft 6D pose estimation remains difficult under weak texture, thin structures, illumination variation, and occlusion.
G6D is a learning‑free, geometry‑driven RGB‑D 6D pose solver designed for robotic manipulation. It generates pose hypotheses via template‑based geometric matching and refines them using silhouette and depth consistency, requiring only an RGB‑D observation, an object mask, camera intrinsics, and a CAD model. The method offers adjustable accuracy‑computation trade‑offs, can run on CPU without GPUs, and has shown strong performance on LineMOD and BOP19 datasets, as well as in real‑world pick‑and‑place experiments.
arXiv:2512. 16919v2 Announce Type: replace-cross Abstract: Perceiving and reconstructing 3D scene geometry from visual inputs is crucial for autonomous driving.
GenCOPE introduces a synthetic-to-real (Syn2Real) approach for category-level object pose estimation (COPE) that eliminates the need for labor-intensive real-world data collection. By learning domain-invariant representations through 2D and 3D semantic consistency constraints and employing an end-to-end pose regression framework with 2D-3D cross consistency, the model achieves robust generalization across synthetic and real domains. The architecture relies solely on global features, resulting in a lightweight and efficient design validated on REAL275, Wild6D, and real-world robotic manipulation scenes.
arXiv:2607. 13449v1 Announce Type: cross Abstract: 6-DoF pose estimation is a critical task in autonomous rendezvous and proximity operations.
RYOPO is an end‑to‑end query‑based RGB‑D set predictor that jointly detects, segments, and estimates 9‑DoF poses of unseen instances within known categories without relying on external instance segmentation or CAD priors. It uses shared image and scene encoding, a query‑conditioned geometry pathway, and object‑centric refinement with pose‑conditioned cross‑attention to achieve accurate pose estimation. On benchmark datasets such as NOCS, REAL275, and HouseCat6D, RYOPO outperforms published methods and runs in real time at 31.8 FPS on an RTX A6000.
arXiv:2610.03717v1 Announce Type: cross Abstract: This paper examines the role of Novel View Synthesis (NVS) in geometric representation learning. In principle, NVS should reason about 3D scene struc...
arXiv:2605.31577v2 Announce Type: replace Abstract: Recent feedforward 3D reconstruction methods predict point maps and estimate global 3D geometry remarkably well. However, their predictions still e...
The paper introduces G2G, a method that leverages known intra-group geometry to estimate the relative 6-DoF pose between two image groups, a key problem in cross-sequence relocalization and multi-camera rig odometry. G2G keeps a frozen foundation model and adds three lightweight trainable modules—a perceiver resampler, a cross-group bridge with merged self-attention, and a multi-frame pose head—totaling about 32 M parameters, less than 6% of the full model. Evaluated on four diverse datasets covering indoor/outdoor simulation, real-world cross-season capture, and zero-shot sim-to-real transfer, G2G achieves state‑of‑the‑art accuracy on both pose estimation tasks while only requiring supervision from relative poses.
CrossDepth introduces geometry-constrained attention for multi-view surround depth estimation, addressing cross-image inconsistencies caused by varying camera intrinsics and limited receptive fields. The method conditions features on per-pixel camera-aware ray embeddings and extends pixel context via cross-image attention limited to geometrically plausible regions. Trained self-supervised with photometric consistency, it achieves better depth accuracy and consistency on DDAD and nuScenes compared to existing self-supervised approaches.
The paper introduces Epipolar Distillation (EpiDistill), a method that transfers scale‑aware geometric priors from multi‑view models to monocular depth foundation models using Rectified Stereo Tokens. By preserving epipolar attention patterns, the single‑view model maintains geometric consistency without needing multi‑view inputs during inference. Experiments show significant improvements in zero‑shot metric depth estimation on challenging datasets such as ETH3D and DIODE, and the approach consistently boosts performance of state‑of‑the‑art ViT‑based models like UniDepthV2 and DepthPro.