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Recent advances in machine learning have led to increased interest in solving visual computing problems using methods that employ coordinate‐based neural networks. These methods, which we call neural fields, parameterize physical properties of scenes or objects across space and time. They have seen widespread success in problems such as 3D shape and image synthesis, animation of human bodies, 3D reconstruction, and pose estimation. Rapid progress has led to numerous papers, but a consolidation of the discovered knowledge has not yet emerged. We provide context, mathematical grounding, and a review of over 250 papers in the literature on neural fields. In Part I, we focus on neural field techniques by identifying common components of neural field methods, including different conditioning, representation, forward map, architecture, and manipulation methods. In Part II, we focus on applications of neural fields to different problems in visual computing, and beyond (e.g., robotics, audio). Our review shows the breadth of topics already covered in visual computing, both historically and in current incarnations, and highlights the improved quality, flexibility, and capability brought by neural field methods. Finally, we present a companion website that acts as a living database that can be continually updated by the community. [ABSTRACT FROM AUTHOR]

CSIRO has extensive experience and track record in turning powders into products. Through the CSIRO Lab 22 and associations, the teams in CSIRO are tailoring metal powders to processes, repurposing waste for additive manufacturing (AM) processes and generating unique materials, products and processes for Australia. The laboratory also houses Australia's own metal printer from Aurora Labs that can operate in both direct laser melting (DLM) and laser powder bed fusion (LPBF) modes. This paper discusses capability and case study examples, with some scientific detail, as we work to mainstream and industrialise AM. [ABSTRACT FROM AUTHOR]