PIXIE: 組み立て欠陥を有する未見物体のためのゼロショット・テクスチャ非依存6D姿勢推定フレームワーク
PIXIE: A Zero-Shot texture-invariant 6D pose estimation framework for unseen objects with assembly defects
テクスチャのない3DモデルとRGB画像のみから、未見物体の6D姿勢をゼロショットで推定するフレームワークを提案。合成レンダリングと特徴マッチングにより2D-3D対応を取得し、PnPで姿勢を計算する。
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著者: Leon Jungemeyer, Alejandro Magaña, Gautham Mohan, Matthias Karl, Daniel Werdehausen
分類: cs.CV, cs.RO
原文アブストラクト
6D pose estimation remains a key challenge in robotics and computer vision, particularly in industrial environments. The deployment of currently available data-driven methods is often limited by resource-intensive data pipelines, reliance on textured 3D models, and sensitivity to geometric deviations caused by damages or assembly defects. We present PIXIE, a zero-shot framework that estimates the 6D pose of an object from an RGB image using only an untextured 3D model. Synthetic depth and normal maps are rendered from sampled reference viewpoints and matched to the query image via a pretrained cross-modality feature matcher. Matched keypoints are back-projected to obtain 2D--3D correspondences for PnP-based pose estimation. Relying exclusively on geometry makes the method inherently robust to lighting and texture variation, while correspondence filtering handles geometric deviations between the model and physical object. We evaluate on widely-used public benchmarks, reporting state-of-the-art results on texture-less objects without object-specific training, and introduce a novel dataset with assembly defects, texture variations, and occlusion to demonstrate real-world applicability.
