← Back to all news
arXiv AI By Yonglong Zhang, Zongwu Xie, Yang Liu

Polarity--Time Interaction and Multi-Level Reliability for Event-Based Spacecraft Pose Estimation

Read the original on arXiv AI →

The paper introduces a reliability‑aware framework for spacecraft pose estimation that integrates polarity–time localization, geometric recovery, event–CAD refinement, and trajectory optimization. It employs a three‑branch representation of positive, negative, and recent events, cross‑branch attention, and spatiotemporal aggregation, followed by a semantic‑point head for precision estimation. The system achieves a pose score of 0.0474 on the SPADES challenge, outperforming prior methods and demonstrating significant improvements through ablation studies.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Computer Vision
Oct 28

TALON: Token-Aligned Lightweight Adapters for 6-DoF Spacecraft Pose Estimation

TALON introduces Token‑Aligned Lightweight Adapters for 6‑DoF spacecraft pose estimation, inserting spatiotemporal 3D adapters before self‑attention layers of a frozen Vision Transformer. A patch‑token alignment loss grounds adapted features to keypoint structure, enabling the frozen backbone to reason over temporally enriched tokens with minimal added parameters. On the SPADES and SwissCube datasets, TALON cuts pose error by 50% and improves ADD‑0.1d accuracy by 21.8%, respectively, while also achieving significant gains in zero‑shot sim‑to‑real cross‑domain evaluation.

By Abid Ali, Nidhal Eddine Chenni, Arunkumar Rathinam, Djamila Aouada
llmscomputer-visionroboticsfine-tuningbenchmarkssafetydata-engineering
More like this →
arXiv Computer Vision
Sep 28

Reliability-Regulated Trajectory Optimization for Progressive COLMAP-Free 3D Gaussian Splatting

The paper introduces a reliability-regulated trajectory optimization framework for progressive COLMAP‑free 3D Gaussian Splatting (3DGS). It uses a self‑supervised bidirectional cycle‑consistency mechanism to control camera trajectory estimation through forward motion propagation and retrospective trajectory correction, thereby reducing error compounding without external priors. Experiments on Tanks and Temples and CO3D‑V2 demonstrate improved camera trajectory accuracy and novel‑view rendering quality compared to existing unposed baselines.

By Zijian Wu, Jinliang Wang, Zidian Lin, Ying Song, Ziqian Lu, Hanjie Ma, Zhen Ye, Mingfeng Jiang
benchmarks
More like this →
arXiv Computer Vision
Sep 21

Refining Ground Truth Poses in Autonomous Driving Datasets via Neural Rendering

arXiv:2504.15776v2 Announce Type: replace Abstract: Public autonomous driving datasets underpin the training and benchmarking of perception, mapping, and localization algorithms, yet residual inaccur...

By Quentin Herau, Nathan Piasco, Moussab Bennehar, Luis Rold\~ao, Dzmitry Tsishkou, Bingbing Liu, Cyrille Migniot, Pascal Vasseur, C\'edric Demonceaux
agentsroboticsmultimodalbenchmarks
More like this →
arXiv Computer Vision
Sep 17

GeoCond: A Conditioning-Aware Reliability Adapter for Feed-Forward 3D Reconstruction

GeoCond is a lightweight reliability adapter that enhances frozen feed‑forward 3D reconstruction backbones by reading their predicted geometry to produce pose‑level uncertainty and a refinement gate. It can be trained using permutation‑orbit variance, ground‑truth pose error, or cycle residuals from unlabeled pose graphs, and at inference requires only a single backbone pass plus a small MLP. On the VGGT backbone, GeoCond reduces out‑of‑distribution AUSE from 0.32 to 0.20, transfers zero‑shot to outdoor extreme‑view scenes, and prevents collapse from uniform bundle adjustment, while also enabling gated refinement, pose‑graph weighting, calibration, curation, and capture decisions.

By David Ahmedt-Aristizabal, Mohammad Ali Armin, Russell Tsuchida, Lars Petersson
More like this →