PoseGAM: Robust Unseen Object Pose Estimation via Geometry-Aware Multi-View Reasoning

TL;DR

PoseGAM introduces a geometry-aware multi-view framework for robust 6D object pose estimation of unseen objects, eliminating explicit feature matching and leveraging synthetic data for improved generalization.

Contribution

It proposes a novel multi-view approach that directly predicts object pose using geometry information without explicit matching, enhancing robustness and generalization.

Findings

Achieves state-of-the-art performance with 5.1% AR improvement.

Demonstrates strong generalization to unseen objects.

Constructed a large synthetic dataset with 190k objects.

Abstract

6D object pose estimation, which predicts the transformation of an object relative to the camera, remains challenging for unseen objects. Existing approaches typically rely on explicitly constructing feature correspondences between the query image and either the object model or template images. In this work, we propose PoseGAM, a geometry-aware multi-view framework that directly predicts object pose from a query image and multiple template images, eliminating the need for explicit matching. Built upon recent multi-view-based foundation model architectures, the method integrates object geometry information through two complementary mechanisms: explicit point-based geometry and learned features from geometry representation networks. In addition, we construct a large-scale synthetic dataset containing more than 190k objects under diverse environmental conditions to enhance robustness and generalization. Extensive evaluations across multiple benchmarks demonstrate our state-of-the-art performance, yielding an average AR improvement of 5.1% over prior methods and achieving up to 17.6% gains on individual datasets, indicating strong generalization to unseen objects. Project page: https://windvchen.github.io/PoseGAM/ .