Not Your Stereo-Typical Estimator: Combining Vision and Language for Volume Perception

2026-04-10

AI Summary

This work addresses the inherent ambiguity in estimating object volume from single-view images due to missing depth information, a challenge that existing approaches either tackle through complex 3D reconstruction or struggle to model visual uncertainty effectively. The paper proposes a novel multimodal framework that, for the first time, integrates implicit geometric cues from stereo vision with explicit category and volumetric priors embedded in natural language. Specifically, depth features extracted from stereo image pairs and semantic representations derived from textual prompts are aligned via a projection layer and jointly regressed to predict object volume. By transcending the limitations of purely visual paradigms, the method significantly outperforms vision-only baselines on public benchmarks, demonstrating that textual guidance effectively enhances both the accuracy and contextual awareness of volume estimation.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• volume estimation
• computer vision
• stereo vision
• single-view ambiguity
• 3D reconstruction

Innovation

• volume estimation
• stereo vision
• vision-language fusion
• multimodal representation
• text-guided perception