Neural Cellular Automata (NCAs) have been proven effective in simulating\nmorphogenetic processes, the continuous construction of complex structures from\nvery few starting cells. Recent developments in NCAs lie in the 2D domain,\nnamely reconstructing target images from a single pixel or infinitely growing\n2D textures. In this work, we propose an extension of NCAs to 3D, utilizing 3D\nconvolutions in the proposed neural network architecture. Minecraft is selected\nas the environment for our automaton since it allows the generation of both\nstatic structures and moving machines. We show that despite their simplicity,\nNCAs are capable of growing complex entities such as castles, apartment blocks,\nand trees, some of which are composed of over 3,000 blocks. Additionally, when\ntrained for regeneration, the system is able to regrow parts of simple\nfunctional machines, significantly expanding the capabilities of simulated\nmorphogenetic systems. The code for the experiment in this paper can be found\nat: https://github.com/real-itu/3d-artefacts-nca.\n