  Neural implicit fields have been a de facto standard in novel view synthesis.
Recently, there exist some methods exploring fusing multiple modalities within
a single field, aiming to share implicit features from different modalities to
enhance reconstruction performance. However, these modalities often exhibit
misaligned behaviors: optimizing for one modality, such as LiDAR, can adversely
affect another, like camera performance, and vice versa. In this work, we
conduct comprehensive analyses on the multimodal implicit field of LiDAR-camera
joint synthesis, revealing the underlying issue lies in the misalignment of
different sensors. Furthermore, we introduce AlignMiF, a geometrically aligned
multimodal implicit field with two proposed modules: Geometry-Aware Alignment
(GAA) and Shared Geometry Initialization (SGI). These modules effectively align
the coarse geometry across different modalities, significantly enhancing the
fusion process between LiDAR and camera data. Through extensive experiments
across various datasets and scenes, we demonstrate the effectiveness of our
approach in facilitating better interaction between LiDAR and camera modalities
within a unified neural field. Specifically, our proposed AlignMiF, achieves
remarkable improvement over recent implicit fusion methods (+2.01 and +3.11
image PSNR on the KITTI-360 and Waymo datasets) and consistently surpasses
single modality performance (13.8% and 14.2% reduction in LiDAR Chamfer
Distance on the respective datasets).
