ROAD: Learning an Implicit Recursive Octree Auto-Decoder to Efficiently Encode 3D Shapes

Understand

Compact and accurate representations of 3D shapes are central to many perception and robotics tasks.

• State-of-the-art learning-based methods can reconstruct single objects but scale poorly to large datasets.
• We present a novel recursive implicit representation to efficiently and accurately encode large datasets of complex 3D shapes by recursively traversing an implicit octree in latent space.
• Our implicit Recursive Octree Auto-Decoder (ROAD) learns a hierarchically structured latent space enabling state-of-the-art reconstruction results at a compression ratio above 99%.