Complete 3D scene using only images having occlusions by masked autoencoder with VoxFormer

Complete 3D scene using only images having occlusions by masked autoencoder with VoxFormer

VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion

arXiv paper abstract https://arxiv.org/abs/2302.12251

arXiv PDF paper https://arxiv.org/pdf/2302.12251.pdf

... complete 3D geometry of occluded objects and scenes ... is vital for recognition and understanding.

... propose VoxFormer, a Transformer-based semantic scene completion framework that can output complete 3D volumetric semantics from only 2D images.

... framework adopts a two-stage design where ... start from a sparse set of visible and occupied voxel queries from depth estimation, followed by a densification stage that generates dense 3D voxels from the sparse ones.

A key idea of this design is that the visual features on 2D images correspond only to the visible scene structures rather than the occluded or empty spaces.

... Once ... obtain the set of sparse queries, ... apply a masked autoencoder design to propagate the information to all the voxels by self-attention.

... VoxFormer outperforms the state of the art with a relative improvement of 20.0% in geometry and 18.1% in semantics ...

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