ConceptFusion: Open-set Multimodal 3D Mapping
(2302.07241)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 (1) fundamentally open-set, enabling reasoning beyond a closed set of concepts and (ii) inherently multimodal, 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. For more information, visit our project page https://concept-fusion.github.io or watch our 5-minute explainer video https://www.youtube.com/watch?v=rkXgws8fiDs
Overview
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ConceptFusion introduces an open-set multimodal approach to 3D mapping that extends beyond fixed labels and utilizes diverse foundation models like CLIP, DINO, and AudioCLIP.
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The paper details a novel zero-shot pixel-aligned feature extraction technique that enables detailed and efficient scene representation, significantly improving real-world task performance in areas like robot manipulation and autonomous driving.
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Extensive evaluation on diverse datasets such as UnCoCo, ScanNet, and SemanticKITTI demonstrates that ConceptFusion achieves superior performance in 3D IoU and detection accuracy, outperforming existing baselines by considerable margins.
ConceptFusion: Open-Set Multimodal 3D Mapping
The paper introduces ConceptFusion, an innovative approach to 3D mapping that addresses limitations of previous methods by enabling open-set and multimodal scene representations. Utilizing foundation models like CLIP, DINO, and AudioCLIP, ConceptFusion constructs 3D maps that can be queried using various modalities such as text, images, audio, or even clicks on the 3D map. These capabilities mark a significant advancement over traditional systems, which are constrained to closed-set reasoning and limited query modalities.
Core Contributions
Open-Set and Multimodal 3D Mapping: ConceptFusion extends the concept representation beyond a fixed set of labels predefined during training. By leveraging diverse foundation models, it enables a wide range of concepts to be queried in real-time without additional training or fine-tuning. This flexibility allows robots to interpret and interact with novel objects and scenarios efficiently.
Zero-Shot Pixel-Aligned Feature Extraction: The paper details a novel technique to compute pixel-aligned features from global and local embeddings. Using class-agnostic mask proposals and combining global context with local features, ConceptFusion retains a rich understanding of fine-grained and long-tailed concepts. This feature extraction mechanism is key to its performance, especially in zero-shot scenarios.
Robust Performance on Real-World Tasks: The paper demonstrates the efficacy of ConceptFusion across various real-world datasets and tasks, including robot manipulation and autonomous driving. By efficiently integrating modern SLAM techniques and foundation features, ConceptFusion shows significant improvements over existing models, especially in tasks requiring semantic understanding and spatial reasoning.
Evaluation and Results
The authors provide extensive evaluation on the UnCoCo dataset, which involves a diverse range of objects and scenarios captured in real-world settings. ConceptFusion exhibits superior performance in terms of 3D IoU and detection accuracy against baselines like LSeg, OpenSeg, and MaskCLIP. Specifically, ConceptFusion outperforms these approaches by over 40% margin on 3D IoU, highlighting its effectiveness in retaining complex concepts without the drawbacks of fine-tuning.
Additionally, ConceptFusion is tested on established datasets like ScanNet, Replica, and SemanticKITTI for open-set semantic segmentation. The zero-shot capabilities of ConceptFusion lead to competitive performance against privileged models. This robustness is further validated through practical deployments, such as real-world tabletop manipulation and autonomous navigation tasks, where the system showed reliable and efficient object identification and query handling.
Implications and Future Directions
The practical implications of ConceptFusion are far-reaching. In autonomous navigation, ConceptFusion enables vehicles to respond to open-set textual queries, enhancing their operational scope. In assistive robotics, the ability to interpret novel objects using multimodal inputs can significantly improve interaction richness and usability.
Theoretically, ConceptFusion bridges the gap between the rich representational capacity of foundation models and the structured spatial understanding needed for robotics. This synergy opens avenues for more sophisticated AI systems that can comprehend and navigate complex environments with minimal pre-defined knowledge.
Future developments could explore deeper integration with LLMs, enriching task-level planning and contextual query parsing. Moreover, addressing the limitations in memory and computation through more efficient algorithms and hardware acceleration could further enhance ConceptFusion's applicability in resource-constrained environments. Additionally, investigating the potential biases in foundation models and developing strategies for AI safety and alignment remain critical to ensuring robust and ethical deployment of such advanced systems.
In summary, ConceptFusion represents a significant advance in the field of 3D mapping and semantic reasoning, providing robust open-set, multimodal query capabilities that can be leveraged across a wide range of real-world applications in AI and robotics.