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Accurate and dense mapping in large-scale environments is essential forvarious robot applications. Recently, implicit neural signed distance fields(SDFs) have shown promising advances in this task. However, most existingapproaches employ projective distances from range data as SDF supervision,introducing approximation errors and thus degrading the mapping quality. Toaddress this problem, we introduce N3-Mapping, an implicit neural mappingsystem featuring normal-guided neural non-projective signed distance fields.Specifically, we directly sample points along the surface normal, instead ofthe ray, to obtain more accurate non-projective distance values from rangedata. Then these distance values are used as supervision to train the implicitmap. For large-scale mapping, we apply a voxel-oriented sliding windowmechanism to alleviate the forgetting issue with a bounded memory footprint.Besides, considering the uneven distribution of measured point clouds, ahierarchical sampling strategy is designed to improve training efficiency.Experiments demonstrate that our method effectively mitigates SDF approximationerrors and achieves state-of-the-art mapping quality compared to existingapproaches.

N$^{3}$-Mapping: Normal Guided Neural Non-Projective Signed Distance Fields for Large-scale 3D Mapping