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.
By Hakjin Lee, Junghoon Seo, Jaehoon Sim
arXiv:2610.09534v1 Announce Type: new
Abstract: Rotational symmetry is an important prior in 6D pose estimation, improving pose accuracy and supporting symmetry-aware evaluation. However, current sym...
By Mengxin Zhang, Yulin Wang, Chen Luo, Yongzhe Li, Yijun Zhou
The paper proposes a data‑centric optimization for object pose estimation that uses a physically grounded rotation representation based on principal axes alignment. By aligning an object's coordinate system with its inertial principal axes, the method achieves inherent stability, symmetry‑aware canonicalization, and framework agnosticism, allowing it to be applied at the dataset level without modifying existing networks. Experiments on category‑level and instance‑level models show consistent accuracy improvements while preserving baseline network integrity.
By Wei Chen, Tao Zhen, Zhongchen Shi, Jing Zhang, Liang Xie, Erwei Yin
arXiv:2607.09985v2 Announce Type: replace
Abstract: Object pose estimation is a fundamental problem in 3D vision. Although recent state-of-the-art approaches achieve strong performance, generalizatio...
By Yang You, Yi Du, Cole Harrison, Leonidas Guibas
arXiv:2504.07335v4 Announce Type: replace
Abstract: We propose DLTPose, a novel method for 6DoF object pose estimation from RGBD images that combines the accuracy of sparse keypoint methods with the...
By Akash Jadhav, Michael Greenspan
arXiv:2604.28130v4 Announce Type: replace
Abstract: Recent methods for arbitrary-skeleton motion capture from monocular video follow a factorized pipeline, where a Video-to-Pose network predicts join...
By Kehong Gong, Zhengyu Wen, Dao Thien Phong, Mingxi Xu, Weixia He, Qi Wang, Ning Zhang, Zhengyu Li, Guanli Hou, Dongze Lian, Xiaoyu He, Mingyuan Zhang, Hanwang Zhang
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.
By Yixuan Liang (Tsinghua University), William Chen (Sapient Intelligence), Yunan Wang (Tsinghua University), Jizhou Yan (Tsinghua University), Zhao Jin (Tsinghua University), Changling Liu (Sapient Intelligence), Chuxiong Hu (Tsinghua University)
SUFLECA is a weakly supervised framework that improves zero‑shot CAD‑to‑image alignment by scaling geometry‑grounded feature learning using Normalized Object Coordinates across up to 12 real and synthetic datasets. It introduces a geometrically consistent matching algorithm that reliably establishes CAD‑to‑image correspondences, enabling accurate, sub‑second alignment without iterative pose refinement. On the ScanNet25k benchmark, SUFLECA achieves 32.8%/42.6% category/instance accuracy, outperforming the strongest zero‑shot baseline by 9.7/12.5 percentage points and surpassing existing pose‑supervised methods for the first time.
By Saad Ejaz, Miguel Fernandez-Cortizas, Javier Civera, Holger Voos, Jose Luis Sanchez-Lopez
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.
By Jian Liu, Wei Sun, Zhenqi Dai, Hui Yang, Jian Xiao, Nicu Sebe, Na Zhao
Category-level object pose estimation seeks to recover a similarity transform $(R,t,s)$ for unseen instances without instance-specific CAD models. Most competitive methods are correspondence-based: pr...
PriorPose introduces a reference-guided correspondence framework for category-level object pose estimation that jointly performs canonicalization and alignment in a shared feature space. By embedding partial observations and a category prior as token sets in a seeded transformer, the network predicts per-point NOCS fields and a canonical deformation, while a deep pose head regresses the similarity transform. A two-part shape consistency objective couples correspondence, deformation, and pose, reducing reliance on memorized canonical orientations and avoiding error cascades, leading to state-of-the-art results on standard and larger-category benchmarks, especially under strict pose thresholds.
By Yihan Chen, Huan Ren, Wenfei Yang, Hang Du, Tianzhu Zhang, Feng Wu
arXiv:2601.13913v3 Announce Type: replace
Abstract: We consider monocular 3D human pose estimation (HPE), where the goal is to predict 3D human skeletal joints from a single 2D image, typically via 2...
By Pavlo Melnyk, Cuong Le, Urs Waldmann, Per-Erik Forss\'en, Bastian Wandt