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Contemporary registration devices for 3D visual information, such as LIDARsand various depth cameras, capture data as 3D point clouds. In turn, suchclouds are challenging to be processed due to their size and complexity.Existing methods address this problem by fitting a mesh to the point cloud andrendering it instead. This approach, however, leads to the reduced fidelity ofthe resulting visualization and misses color information of the objects crucialin computer graphics applications. In this work, we propose to mitigate thischallenge by representing 3D objects as Neural Radiance Fields (NeRFs). Weleverage a hypernetwork paradigm and train the model to take a 3D point cloudwith the associated color values and return a NeRF network's weights thatreconstruct 3D objects from input 2D images. Our method provides efficient 3Dobject representation and offers several advantages over the existingapproaches, including the ability to condition NeRFs and improvedgeneralization beyond objects seen in training. The latter we also confirmed inthe results of our empirical evaluation.

Points2NeRF: Generating Neural Radiance Fields from 3D point cloud