MegaNorm: Local Patch Embeddings for Efficient and Robust Point Normal Orientation at Super-Large Scale

Normal orientation has been studied extensively, yet reliably orienting normals on scene-scale point clouds remains challenging. Most existing approaches are tailored to watertight surfaces and often degrade on non-watertight scenes. The few methods that do support non-watertight geometry are typically too slow to handle very large inputs. In this paper, we propose a robust and efficient approach for orienting normals on super-large, non-watertight scenes. Our key insight is that, although global shapes vary widely, local geometric structures are often similar. Based on this observation, we adopt a divide-and-conquer strategy that decomposes the input into smaller patches. We then train a self-conditioning neural network to orient normals within each patch, enabling high efficiency and straightforward parallelization. The remaining challenge is to enforce global consistency across patches. To this end, we train a second network to predict pairwise consistency weights for adjacent patches under potential flipping. Using these weights, we cast global patch reconciliation as a 0-1 integer programming with <1000 binary variables, which can be solved efficiently with off-the-shelf solvers. Since both networks operate only on local patches, our method generalizes well across datasets. Experiments on diverse scene-level data demonstrate that our approach is robust and scales to inputs with up to 50 million points. Code and pre-trained models are publicly available at https://github.com/zd-lee/MegaNorm.