ConceptFusion: Open-set Multimodal 3D Mapping

Krishna Murthy Jatavallabhula, Alihusein Kuwajerwala, Qiao Gu, Mohd Omama, Tao Chen, Alaa Maalouf, Shuang Li, Ganesh Subramanian Iyer, Soroush Saryazdi, Nikhil Varma Keetha, Ayush Tewari, Joshua B. Tenenbaum, Celso M de Melo, Madhava Krishna, Liam Paull, Florian Shkurti, Antonio Torralba

Keywords: 3D mapping, foundation models, SLAM, vision-language, audio, multimodal, perception

TL;DR: ConceptFusion builds open-set 3D maps that can be queried via text, click, image, or audio.

Abstract:

Building 3D maps of the environment is central to robot navigation, planning, and interaction with objects in a scene. Most existing approaches that integrate semantic concepts with 3D maps largely remain confined to the closed-set setting: they can only reason about a finite set of concepts, pre-defined at training time. Further, these maps can only be queried using class labels, or in recent work, using text prompts.

We address both these issues with ConceptFusion, a scene representation that is: (i) fundamentally open-set, enabling reasoning beyond a closed set of concepts (ii) inherently multi-modal, enabling a diverse range of possible queries to the 3D map, from language, to images, to audio, to 3D geometry, all working in concert. ConceptFusion leverages the open-set capabilities of today’s foundation models pre-trained on internet-scale data to reason about concepts across modalities such as natural language, images, and audio. We demonstrate that pixel-aligned open-set features can be fused into 3D maps via traditional SLAM and multi-view fusion approaches. This enables effective zero-shot spatial reasoning, not needing any additional training or finetuning, and retains long-tailed concepts better than supervised approaches, outperforming them by more than 40% margin on 3D IoU. We extensively evaluate ConceptFusion on a number of real-world datasets, simulated home environments, a real-world tabletop manipulation task, and an autonomous driving platform. We showcase new avenues for blending foundation models with 3D open-set multimodal mapping.

Community Implementations: [ 7 code implementations](https://www.catalyzex.com/paper/arxiv:2302.07241/code)