Zero-Shot Multi-Object Shape Completion

• URL: http://arxiv.org/abs/2403.14628v1
• Date: Thu, 21 Mar 2024 17:59:59 GMT
• Title: Zero-Shot Multi-Object Shape Completion
• Authors: Shun Iwase, Katherine Liu, Vitor Guizilini, Adrien Gaidon, Kris Kitani, Rares Ambrus, Sergey Zakharov,
• Abstract summary: We present a 3D shape completion method that recovers the complete geometry of multiple objects in complex scenes from a single RGB-D image. Our method outperforms the current state-of-the-art on both synthetic and real-world datasets.
• Score: 59.325611678171974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a 3D shape completion method that recovers the complete geometry of multiple objects in complex scenes from a single RGB-D image. Despite notable advancements in single object 3D shape completion, high-quality reconstructions in highly cluttered real-world multi-object scenes remains a challenge. To address this issue, we propose OctMAE, an architecture that leverages an Octree U-Net and a latent 3D MAE to achieve high-quality and near real-time multi-object shape completion through both local and global geometric reasoning. Because a na\"ive 3D MAE can be computationally intractable and memory intensive even in the latent space, we introduce a novel occlusion masking strategy and adopt 3D rotary embeddings, which significantly improves the runtime and shape completion quality. To generalize to a wide range of objects in diverse scenes, we create a large-scale photorealistic dataset, featuring a diverse set of 12K 3D object models from the Objaverse dataset which are rendered in multi-object scenes with physics-based positioning. Our method outperforms the current state-of-the-art on both synthetic and real-world datasets and demonstrates a strong zero-shot capability.

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