Unified Category-Level Object Detection and Pose Estimation from RGB Images using 3D Prototypes

• URL: http://arxiv.org/abs/2508.02157v1
• Date: Mon, 04 Aug 2025 07:57:39 GMT
• Title: Unified Category-Level Object Detection and Pose Estimation from RGB Images using 3D Prototypes
• Authors: Tom Fischer, Xiaojie Zhang, Eddy Ilg,
• Abstract summary: We introduce a unified model that integrates detection and pose estimation into a single framework for RGB images.<n>Our approach achieves state-of-the-art results for RGB category-level pose estimation on REAL275.
• Score: 5.224479258519442
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recognizing objects in images is a fundamental problem in computer vision. Although detecting objects in 2D images is common, many applications require determining their pose in 3D space. Traditional category-level methods rely on RGB-D inputs, which may not always be available, or employ two-stage approaches that use separate models and representations for detection and pose estimation. For the first time, we introduce a unified model that integrates detection and pose estimation into a single framework for RGB images by leveraging neural mesh models with learned features and multi-model RANSAC. Our approach achieves state-of-the-art results for RGB category-level pose estimation on REAL275, improving on the current state-of-the-art by 22.9% averaged across all scale-agnostic metrics. Finally, we demonstrate that our unified method exhibits greater robustness compared to single-stage baselines. Our code and models are available at https://github.com/Fischer-Tom/unified-detection-and-pose-estimation.

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