Where Appearance Fails, Geometry Recognizes: A CAD-Free 3D Shape Prior That Complements Vision Foundation Models

SummaryAI generated

To address the challenge of object recognition in labeled and unlabeled training sets for manufacturing and service robots, researchers proposed a 3D shape prior method that does not require a CAD model. This method uses 3D Gaussian Splatting to reconstruct objects into shape prototypes for each category, and fuses them with frozen DINOv2 image features. Experimental results show that on the T-LESS dataset, recognition performance using only geometric information is comparable to that with RGB-D depth and CAD models; on the HOPE dataset, a recognition rate of 0.920 can be achieved with only geometric information, which is significantly better than 0.832 for image-based recognition alone. For industrial parts with easily confused shapes and no texture, the fusion method provides limited gains but maintains consistent performance. This prior has complementary rather than uniform additive properties, effectively saving more image failures and improving performance under partial occlusion. The study confirms that the value of this method lies in geometry itself, not in rendering pixels; frozen features are almost unaffected by lighting conditions. This research focuses solely on recognition tasks.

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Key entitiesKEY ENTITIES

3D Gaussian SplattingDINOv2HOPET-LESS

Coverage · reports per dayLANGUAGE SPLIT

Entity relations

SignalsSIGNALS

Keyword heat

3D Gaussian Splatting1
DINOv21
HOPE1
T-LESS1

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A arXiv cs.CV en 2026-09-07 12:00

For the problem of object recognition in manufacturing and service robots with unlabeled training datasets, the study proposes a 3D shape prior method that does not require CAD models. This method reconstructs objects into shape prototypes for each category using 3D Gaussian Splatting (3DGS) and fuses them with frozen DINOv2 image features. Experiments show that RGB-D depth, 3DGS, and CAD geometric information provide comparable recognition performance on the T-LESS dataset; on the HOPE dataset, only geometric information was sufficient to achieve a recognition rate of 0.920, significantly better than 0.832 for image-based recognition alone. For industrial parts with easily confused shapes and no texture (T-LESS), the fusion method provides limited benefits but consistent performance. This prior has complementary rather than uniform additive properties, capable of saving more cases where images fail, and its effectiveness increases under partial occlusion. The study confirms that value stems from geometry itself rather than rendering pixels, and frozen feature recognition is almost unaffected by lighting conditions. This research focuses solely on recognition tasks and does not involve…