Neural Explicit Shape Representation
Jun-Hao Wang, Changan Lu, Baoquan Chen, Peng-Shuai Wang
Existing neural 3D representations separately address continuous surface modeling, efficient image synthesis, or object-specific compression. They do not jointly provide the explicit surface queries required by ray tracing and a structured output space that can be predicted across objects. We introduce NeuOctree, a neural explicit representation that directly predicts whether and where a ray intersects a surface, together with its normal and albedo. NeuOctree stores shared features at the corners of sparse octree voxels and interpolates them into a continuous local field. A lightweight decoder with recurrent intersection-guided sampling refines each query near the predicted surface, enabling direct surface rendering without mesh extraction or texture baking and accommodating open, non-watertight geometry. The same octree structure supports both compact per-object optimization and a shared octree CNN that constructs renderable assets from colored point clouds without per-object fitting. Across seven meshes and four storage budgets, NeuOctree achieves the lowest positional error in 24 of 28 comparisons while retaining competitive normal accuracy, and substantially improves joint geometry--appearance reconstruction over N-BVH and VQAD. The feedforward model constructs a representation in 0.05 seconds and transfers from Objaverse to ShapeNet without retraining. We further demonstrate ray-traced rendering and image-conditioned generation, showing that NeuOctree provides a common output space for neural inference and direct surface rendering. Code is available at https://github.com/octree-nn/neural-octree.
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