  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.
