CN-DHF: Compact Neural Double Height-Field Representations of 3D Shapes
This work addresses the need for efficient 3D shape storage and reconstruction in computer graphics and vision, offering a significant improvement over existing compact representations.
The paper tackles the problem of compact 3D shape representation by introducing CN-DHF, a hybrid neural implicit method that reduces reconstruction error by a factor of 2.5 on average compared to state-of-the-art methods with the same parameter count.
We introduce CN-DHF (Compact Neural Double-Height-Field), a novel hybrid neural implicit 3D shape representation that is dramatically more compact than the current state of the art. Our representation leverages Double-Height-Field (DHF) geometries, defined as closed shapes bounded by a pair of oppositely oriented height-fields that share a common axis, and leverages the following key observations: DHFs can be compactly encoded as 2D neural implicits that capture the maximal and minimal heights along the DHF axis; and typical closed 3D shapes are well represented as intersections of a very small number (three or fewer) of DHFs. We represent input geometries as CNDHFs by first computing the set of DHFs whose intersection well approximates each input shape, and then encoding these DHFs via neural fields. Our approach delivers high-quality reconstructions, and reduces the reconstruction error by a factor of 2:5 on average compared to the state-of-the-art, given the same parameter count or storage capacity. Compared to the best-performing alternative, our method produced higher accuracy models on 94% of the 400 input shape and parameter count combinations tested.