The vehicle-drone cooperative delivery mode has demonstrated significant potential in enhancing last-mile delivery efficiency by effectively integrating the transportation advantages of both vehicles and drones. However, increasingly complex last-mile delivery scenarios impose higher requirements on this cooperative approach. To accommodate the demand for routing flexibility arising from distribution network layouts featuring multi-depot collaboration and multi-type rendezvous points used for drone launch and retrieval, this paper explores the collaborative multi-depot vehicle routing problem (MDVRP) with drones, considering multi-type rendezvous points (CMDVRP-D-MTRP). Specifically, vehicles and drones can flexibly select their respective departure and return nodes for each distribution route, and drones can choose rendezvous points from either fixed parking nodes or temporary parking nodes converted from customer nodes that are actually visited by associated vehicles. First, based on the framework of the half-open multi-depot vehicle routing problem (HOMDVRP), the CMDVRP-D-MTRP is formulated as a Mixed-Integer Programming (MIP) model to minimize the total cost, including distribution and vehicle waiting costs. Second, by adapting benchmark instances of the MDVRP, this paper designs test instances tailored to the CMDVRP-D-MTRP covering various scales and distribution features. We apply the Cardinal Optimizer (COPT) solver to validate model feasibility and analyze the impact of problem scale on computational performance. Finally, based on the classification criteria of multi-depot collaboration and multi-type rendezvous points, numerical experiments are conducted across four delivery modes to verify the effectiveness of the proposed model. Compared to the traditional delivery mode that lacks multi-depot collaboration and relies solely on single-type rendezvous points, the proposed model for the CMDVRP-D-MTRP reduces the average total delivery cost by 20.58%.