###### Abstract
This adaptability makes them particularly well-suited to address the challenges of hyperspectral and lidar data fusion within the 3D domain. As noted, there exists a large corpus of previous work related to the learned fusion and exploitation of hyperspectral and lidar data within the 2D image type domain. An article from [PERSON] et al. [7] depicts a recent and mature method. In their method, a central feature extraction network is adversarially trained by two GANs [13] with coupled spatial attention [14] between the hyperspectral and lidar modalities. Simultaneously, a multi-level feature fusion and classification network is used to first generate class probabilities from features provided at multiple depths within the feature extraction network. These probabilities are then combined into a joint class probability distribution and used to generate a semantically labeled pixel map of the input. While mature methods like this and others [15; 16] exist for fusion in the 2D domain, recent work partially within the 3D domain has shown promising initial results. This opens up new avenues for exploring the fusion of hyperspectral and lidar data in this manner and highlights the need for continued research in this area to fully leverage the rich information contained within these two complementary modalities. To our knowledge, [PERSON] et al. [8], the same authors as this article, is the only study that combines 2D hyperspectral image data and 3D lidar point cloud data within a fusion neural network. Their composite fusion [17; 18] style convolutional neural network (CNN) includes both pixel and point convolutional layers. The pixel convolution stream of the network processes hyperspectral images using standard 2D-CNN layers. The point convolution stream of the network uses Kernel Point Convolution (KPConv) [19] layers to process lidar point clouds. The central fusion stream fuses unimodal features from the pixel and point streams, produces multimodal pixel features, and generates a semantic pixel map prediction. The authors introduce a unique point-to-pixel feature discretization method to enable the concatenation-based fusion of the lidar and hyperspectral features. While the resulting 2D-3D fusion network performed competitively, it did not achieve state-of-the-art performance compared to 2D-2D networks; this may be attributed to the discretization of 3D to 2D features. The next natural step is to explore methods for representing hyperspectral data as a 3D point cloud and performing a learned fusion operation entirely within the 3D domain. In this work, we take this next step by extending the fusion network into the 3D domain, presenting our contributions as advancements in the field of hyperspectral and lidar data fusion. The key contributions of this paper can be summarized as follows:  
1. We introduce an innovative method for generating Hyperspectral Point Cloud (HSPC) representations, one focusing on the exploitation of primarily spectral features and another allowing for the utilization of both spectral and structural features (Section 2.1.2). 2. We develop a composite style fusion network based on Kernel Point Convolution (KPConv), as well as other network architectures, which form part of an ablative study aimed at evaluating the performance of unimodal networks in comparison to the multimodal network (Section 2.2.1). This fusion network is the first to perform a learned feature fusion of 3D point clouds in a fully 3D point convolution-based neural network. 3. We propose a unique method for associating hyperspectral point locations with lidar point locations, enabling the generation of a canonical set of points where multimodal features are localized (Section 2.2.2). 4. We offer a comprehensive performance analysis of both unimodal and multimodal networks, discussing their results in the context of our proposed hyperspectral representation and fusion implementation (Section 3).