SHREC 2025: Retrieval of Optimal Objects for Multi-modal Enhanced Language and Spatial Assistance (ROOMELSA)

TL;DR

ROOMELSA is a comprehensive benchmark for evaluating systems that interpret natural language to retrieve specific 3D models within complex, cluttered scenes, emphasizing the integration of visual and language understanding.

Contribution

The paper introduces ROOMELSA, a large-scale benchmark dataset and evaluation framework for natural language-based 3D object retrieval in realistic indoor scenes.

Findings

Coarse object retrieval is largely solved.

A lightweight CLIP-based model performs well but struggles with subtle variations.

Tightly integrating visual and language understanding is crucial.

Abstract

Recent 3D retrieval systems are typically designed for simple, controlled scenarios, such as identifying an object from a cropped image or a brief description. However, real-world scenarios are more complex, often requiring the recognition of an object in a cluttered scene based on a vague, free-form description. To this end, we present ROOMELSA, a new benchmark designed to evaluate a system's ability to interpret natural language. Specifically, ROOMELSA attends to a specific region within a panoramic room image and accurately retrieves the corresponding 3D model from a large database. In addition, ROOMELSA includes over 1,600 apartment scenes, nearly 5,200 rooms, and more than 44,000 targeted queries. Empirically, while coarse object retrieval is largely solved, only one top-performing model consistently ranked the correct match first across nearly all test cases. Notably, a lightweight CLIP-based model also performed well, although it struggled with subtle variations in materials, part structures, and contextual cues, resulting in occasional errors. These findings highlight the importance of tightly integrating visual and language understanding. By bridging the gap between scene-level grounding and fine-grained 3D retrieval, ROOMELSA establishes a new benchmark for advancing robust, real-world 3D recognition systems.