Inspired by the observation that pattern structures high-frequently recur within intra-task also across tasks, we propose a pattern-structure diffusion (PSD) framework to mine and propagate task-specific and task-across pattern structures in the task-level space for joint depth estimation, segmentation and surface normal prediction. To represent local pattern structures, we model them as small-scale graphlets, and propagate them in two different ways, i.e., intra-task and inter-task PSD. For the former, to overcome the limit of the locality of pattern structures, we use the high-order recursive aggregation on neighbors to multiplicatively increase the spread scope, so that long-distance patterns are propagated in the intra-task space. In the inter-task PSD, we mutually transfer the counterpart structures corresponding to the same spatial position into the task itself based on the matching degree of paired pattern structures therein. Finally, the intra-task and inter-task pattern structures are jointly diffused among the task-level patterns, and encapsulated into an end-to-end PSD network to boost the performance of multi-task learning. Extensive experiments on two widely-used benchmarks demonstrate that our proposed PSD is more effective and also achieves the state-of-the-art or competitive results.