PixelDec: Pixel-aligned Superquadric Decomposition from a Single Image

We present PixelDec, an approach to obtain pixel-aligned superquadric decompositions from a single RGB image. Previous approaches for superquadric decomposition have shown how a compact representation of 3D scenes can be useful for different downstream tasks, such as robotic navigation and path planning. However, all existing approaches are deterministic and tackle the problem from a reconstruction perspective, representing with primitives only the observed parts of the objects, and often yielding incomplete and fragmented decompositions. In this work, for the first time, we tackle primitive-based decomposition from a generative perspective, using the compact representation not only as our final output, but also as a compact representation space in which we efficiently learn the distribution of real-world 3D shapes. We train our model on ShapeNet and finetune it on Aria Synthetic Environments, and show that when conditioned on single-view inputs, PixelDec strongly outperforms previous decomposition methods as well as multi-stage pipelines built on state-of-the-art single-image reconstructors. Finally, we show how our method can be practically used for a range of downstream applications.