RefineAny3D: Depth Refinement as Semantic Alignment for Monocular 3D Detection

• URL: http://arxiv.org/abs/2608.09147v1
• Date: Mon, 10 Aug 2026 05:50:37 GMT
• Title: RefineAny3D: Depth Refinement as Semantic Alignment for Monocular 3D Detection
• Abstract summary: Current depth foundation models lack the object-level precision 3D detection demands.<n>We present RefineAny3D, a vision-language model that corrects depth without ever predicting a numerical value.
• Score: 44.58463514444319
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Monocular 3D object detection spans two regimes: closed-set detectors operating within a fixed category vocabulary, and open-vocabulary detectors that localize arbitrary categories by leveraging depth foundation models for 3D geometry. We find that current depth foundation models, despite their strong zero-shot generalization, lack the object-level precision 3D detection demands: substituting a state-of-the-art depth foundation model for a strong detector's predicted depth degrades accuracy, even falling below the detector's own prediction. Rather than pushing detectors or depth models to be more accurate end-to-end, we treat object-level depth refinement as a stand-alone task and present RefineAny3D, a vision-language model that corrects depth without ever predicting a numerical value. Our key insight is that depth error has a direct visual signature in image space: when projected onto the image, a correctly placed box tightly encloses the object, while a too-far box projects too small and a too-close box projects too large. Depth refinement thus reduces to a visual alignment problem rather than a metric regression problem, which we instantiate by extending the VLM's vocabulary with action tokens that replace numerical depth output with categorical decisions, and by supervising the model on a large-scale chain-of-thought dataset that grounds each decision in explicit visual evidence. Applied as a single post-hoc step, RefineAny3D delivers consistent gains across closed-set detectors, open-vocabulary detectors, and 3D auto-labeling tools, and generalizes to novel categories, scenes, and cameras without retraining.

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