Accurate volume estimation of objects from visual data is a long-standing challenge in computer vision with significant applications in robotics, logistics, and smart health. Existing methods often rely on complex 3D reconstruction pipelines or struggle with the ambiguity inherent in single-view images. To address these limitations, we introduce a new method that fuses implicit 3D cues from stereo vision with explicit prior knowledge from natural language text. Our approach extracts deep features from a stereo image pair and a descriptive text prompt that contains the object's class and an approximate volume, then integrates them using a simple yet effective projection layer into a unified, multi-modal representation for regression. We conduct extensive experiments on public datasets demonstrating that our text-guided approach significantly outperforms vision-only baselines. Our findings show that leveraging even simple textual priors can effectively guide the volume estimation task, paving the way for more context-aware visual measurement systems. Code: https://gitlab.com/viper-purdue/stereo-typical-estimator.

翻译：从视觉数据中准确估计物体体积是计算机视觉领域一个长期存在的挑战，在机器人技术、物流以及智能健康等领域具有重要应用。现有方法往往依赖复杂的三维重建流程，或者难以应对单视角图像固有的歧义性。为克服这些局限，我们提出一种新方法，将立体视觉中的隐式三维线索与自然语言文本中的显式先验知识相融合。该方法从立体图像对及包含物体类别与近似体积的描述性文本提示中提取深度特征，并通过一个简单而有效的投影层将其整合为统一的、多模态表示以进行回归。我们在公开数据集上进行了大量实验，结果表明，我们这种基于文本引导的方法显著优于纯视觉基线。我们的发现表明，利用哪怕是简单的文本先验也能有效引导体积估计任务，这为构建更具上下文感知能力的视觉测量系统开辟了道路。代码：https://gitlab.com/viper-purdue/stereo-typical-estimator