3D point clouds are receiving increased attention due to their potential for many important applications, such as real-time 3D immersive telepresence. Compared to traditional video technology, 3D point cloud systems allow free viewpoint rendering, as well as mixing of natural and synthetic objects. However, this improved user experience comes at the cost of increased storage and bandwidth requirements as point clouds are typically represented by the geometry and colour of millions up to billions of 3D points. For this reason, major efforts are being made to develop efficient point cloud compression schemes. The task, however, is very challenging due to the irregular structure of point clouds. To standardize these efforts, the Moving Picture Experts Group (MPEG) launched in January 2017 a call for proposals for 3D point cloud compression technology. In October 2017, the responses were evaluated and the first test model for lossy compression of dynamic point clouds (TMC2) was established. This test model defines a first “common core” algorithm for collaborative work towards the final standard. The aim of OPT-PCC is to contribute to these efforts by developing algorithms that optimize the rate-distortion performance of the test model. OPT-PCC’s objectives are to: 1. O1: build analytical models that accurately describe the effect of the geometry and colour quantization of a 3D point cloud on the bit rate and distortion; 2. O2: develop fast search algorithms that optimize the allocation of the available bit budget between the geometry information and colour information; 3. O3: implement a compression scheme for dynamic 3D point clouds that outperforms the state-of-the-art in terms of rate-distortion performance. The target is to reduce the bit rate by at least 20% for the same reconstruction quality; 4. O4: provide multi-disciplinary training to the researcher in algorithm design, metaheuristic optimisation, computer graphics, and leadership and management skills.