VLRC: Vision-Language Reprojection Consistency as a scalable signal for better feed-forward 3D pretraining

• URL: http://arxiv.org/abs/2607.02707v3
• Date: Wed, 08 Jul 2026 14:37:50 GMT
• Title: VLRC: Vision-Language Reprojection Consistency as a scalable signal for better feed-forward 3D pretraining
• Abstract summary: Vision-Language Reprojection Consistency (VLRC) exploits frozen vision-language representations as semantic multi-view supervision.<n>VLRC integrates seamlessly with both self-supervised monocular reconstruction and supervised-pretrained feed-forward 3D models.<n>Experiments on indoor and outdoor benchmarks demonstrate consistent gains in 3D reconstruction accuracy and zero-shot open-vocabulary 3D semantic segmentation.
• Score: 8.146936489399293
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Feed-forward 3D models are commonly trained using either expensive geometric supervision or self-supervised photometric objectives, both of which provide incomplete learning signals. We introduce Vision-Language Reprojection Consistency (VLRC), a scalable auxiliary objective that exploits frozen vision-language representations as semantic multi-view supervision. Given a predicted 3D reconstruction, VLRC reprojects dense vision-language features across views and enforces feature consistency between corresponding image locations, requiring no additional 3D annotations. The objective integrates seamlessly with both self-supervised monocular reconstruction and supervised-pretrained feed-forward 3D models during unlabeled adaptation. By aligning geometry with language-grounded features, VLRC not only improves depth and camera estimation but also enables more coherent multi-view semantic fusion for open-vocabulary 3D scene understanding. Experiments on indoor and outdoor benchmarks demonstrate consistent gains in 3D reconstruction accuracy and zero-shot open-vocabulary 3D semantic segmentation.

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