SceneComplete: Open-World 3D Scene Completion in Cluttered Real World Environments for Robot Manipulation (2410.23643v3)

Abstract: Careful robot manipulation in every-day cluttered environments requires an accurate understanding of the 3D scene, in order to grasp and place objects stably and reliably and to avoid colliding with other objects. In general, we must construct such a 3D interpretation of a complex scene based on limited input, such as a single RGB-D image. We describe SceneComplete, a system for constructing a complete, segmented, 3D model of a scene from a single view. SceneComplete is a novel pipeline for composing general-purpose pretrained perception modules (vision-language, segmentation, image-inpainting, image-to-3D, visual-descriptors and pose-estimation) to obtain highly accurate results. We demonstrate its accuracy and effectiveness with respect to ground-truth models in a large benchmark dataset and show that its accurate whole-object reconstruction enables robust grasp proposal generation, including for a dexterous hand. We release the code on our website https://scenecomplete.github.io/.

• The paper presents a novel system that completes full 3D scenes using a single RGB-D image to support robotic manipulation.
• It combines vision-language, segmentation, inpainting, and 3D modeling techniques to accurately reconstruct occluded object details.
• Results reveal improved IoU scores and reduced grasp collision rates, demonstrating significant potential for real-world robotic tasks.

Evaluating Open-World 3D Scene Completion for Robotics with SceneComplete

The paper presents "SceneComplete," a system aimed at addressing the challenge of 3D scene completion in complex environments for robotic manipulation. SceneComplete is designed to operate in open-world settings, differentiating itself by constructing a complete, object-segmented 3D model using minimal input — specifically, a single RGB-D image. This approach does not rely on predefined categories of objects, whereas previous methods were often limited by these constraints.

Methodology and Implementation

SceneComplete harnesses multiple pre-trained perception components, combining their individual capabilities into a cohesive system:

1. Vision-LLM (VLM): This model identifies and describes the objects captured in the scene from the input RGB image.
2. Grounded Segmentation Model: It processes these object descriptions, generating image masks that outline each object's extent in the image.
3. Image Inpainting Model: Occluded parts of objects are reconstructed by predicting what these parts look like in a complete view, providing a basis for further 3D modeling.
4. Image-to-3D Model: Constructs textured 3D meshes from the inpainted images, offering a full representation of the objects.
5. Mesh Scaling and Registration: The system scales and registers the meshes accurately within the initial 3D scan's coordinate frame, ensuring the models integrate seamlessly into the reconstructed scene.

This pipeline leverages advances in AI, demonstrating interoperability among diverse pre-trained models to address a complex scene interpretation problem. The design allows future improvements in the underlying perception models to enhance SceneComplete's capabilities seamlessly.

Evaluation and Results

The authors conduct several evaluation regimes to validate SceneComplete:

• Quantitative Analysis: SceneComplete shows a mean Intersection-over-Union (IoU) score of 0.39 on the GraspNet-1B dataset, illustrating heightened accuracy compared to an ablation model without full 3D shape completion.
• Qualitative Assessment: On a custom dataset with unique objects, the system still performs reliably, indicating strong potential for generalization in unseen environments.
• Robust Grasp Planning: The reconstructions facilitate precise grasp proposals, particularly with dexterous hands. The paper notes a significant increase in successful grasp generations—reported as a reduction in collision instances with real objects—from 49% to 26% when using SceneComplete over the ablation model.

Implications and Future Work

The implications of SceneComplete are substantial for robotic applications in unstructured environments. By accurately predicting and reconstructing occluded components of scenes, robots can perform manipulation tasks more reliably, ranging from grasping to complex packing operations.

However, the authors acknowledge several failure points within the model's process cascade, each step of which could propagate modeling errors. For instance, inaccuracies could occur in initial object identification or segmentation, leading to downstream effects in 3D modeling and registration. They suggest that integrating mechanisms for multiple hypothesis generation and uncertainty quantification could enhance robustness.

Conclusion

Through SceneComplete, the authors highlight a path forward for full-scene 3D reconstructions using single-view RGB-D input. The work stands out for its effective integration of multiple AI models, signaling a pivotal step towards scalable and adaptable solutions for real-world robotic manipulation challenges. As foundational models continue to evolve, so too will the accuracy and applicability of systems like SceneComplete in everyday environments.