Beyond Bare Queries: Open-Vocabulary Object Grounding with 3D Scene Graph

• URL: http://arxiv.org/abs/2406.07113v3
• Date: Mon, 16 Sep 2024 15:47:45 GMT
• Title: Beyond Bare Queries: Open-Vocabulary Object Grounding with 3D Scene Graph
• Authors: Sergey Linok, Tatiana Zemskova, Svetlana Ladanova, Roman Titkov, Dmitry Yudin, Maxim Monastyrny, Aleksei Valenkov,
• Abstract summary: We propose a modular approach called BBQ that constructs 3D scene graph representation with metric and semantic edges. BBQ employs robust DINO-powered associations to construct 3D object-centric map. We show that BBQ takes a leading place in open-vocabulary 3D semantic segmentation compared to other zero-shot methods.
• Score: 0.3926357402982764
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Locating objects described in natural language presents a significant challenge for autonomous agents. Existing CLIP-based open-vocabulary methods successfully perform 3D object grounding with simple (bare) queries, but cannot cope with ambiguous descriptions that demand an understanding of object relations. To tackle this problem, we propose a modular approach called BBQ (Beyond Bare Queries), which constructs 3D scene graph representation with metric and semantic edges and utilizes a large language model as a human-to-agent interface through our deductive scene reasoning algorithm. BBQ employs robust DINO-powered associations to construct 3D object-centric map and an advanced raycasting algorithm with a 2D vision-language model to describe them as graph nodes. On the Replica and ScanNet datasets, we have demonstrated that BBQ takes a leading place in open-vocabulary 3D semantic segmentation compared to other zero-shot methods. Also, we show that leveraging spatial relations is especially effective for scenes containing multiple entities of the same semantic class. On challenging Sr3D+, Nr3D and ScanRefer benchmarks, our deductive approach demonstrates a significant improvement, enabling objects grounding by complex queries compared to other state-of-the-art methods. The combination of our design choices and software implementation has resulted in significant data processing speed in experiments on the robot on-board computer. This promising performance enables the application of our approach in intelligent robotics projects. We made the code publicly available at https://linukc.github.io/BeyondBareQueries/.

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