Image-based feature representation plays a critical role in visual localization, enabling robots to estimate their position and orientation in GPS-denied environments. However, this task is often undermined by significant variations in camera viewpoints and scene appearances. Recently, map-free visual relocalization (MFVR) has emerged as a promising paradigm due to its compatibility with lightweight deployment and privacy isolation on mobile devices. In this paper, we propose the Debiased Multiplex Tokenizer (DeMT) as a novel method for versatile and efficient MFVR. Specifically, DeMT performs relative pose regression through an integrated framework built upon a pretrained vision Mamba encoder, comprising three key modules: First, Multiplex Interactive Tokenization yields robust image tokens with non-local affinities and cross-domain descriptions; Second, Debiased Anchor Registration facilitates anchor token matching through proximity graph retrieval and causal pointer attribution; Third, Geometry-Informed Pose Regression empowers multi-layer perceptrons with a gating mechanism and spectral normalization to support both pair-wise and multi-view modes. Extensive evaluations across nine public datasets demonstrate that DeMT substantially outperforms existing baselines and ablation variants in diverse indoor and outdoor environments.

Accurately estimating the 6-DoF pose of objects is a fundamental challenge in computer vision and robotics. While category-level pose estimation based on RGBD data has achieved good performance in recent years, estimating poses solely from RGB images remains a significant challenge. Existing RGB-based category-level methods primarily focus on recovering object point clouds from RGB images, and pose prediction is not performed end-to-end by a network. This paper presents a Category-level and Instance-level Pose Estimation Network (CIPE), which models pose estimation as a set prediction problem and enables direct pose regression from RGB images. To further enhance the network's ability to learn object poses, first, a novel learnable rotation representation that redefines rotation learning within Euclidean space is introduced to facilitate rotation regression. Additionally, we propose a prior-query fusion strategy that utilizes a pre-trained point cloud feature extraction network to integrate categorical object features with bounding boxes, thereby improving the incorporation of category information. Experimental results demonstrate that CIPE significantly outperforms existing RGB-based methods on both category-level and instance-level datasets. The code is available at https://github.com/jialeren/CIPE.