With the autonomy of aerial robots advances in recent years, autonomous drone racing has drawn increasing attention. In a professional pilot competition, a skilled operator always controls the drone to agilely avoid obstacles in aggressive attitudes, for reaching the destination as fast as possible. Autonomous flight like elite pilots requires planning in <inline-formula><tex-math notation="LaTeX">$\mathrm{SE}(3)$</tex-math></inline-formula>, whose non-triviality and complexity hindering a convincing solution in our community by now. To bridge this gap, this letter proposes an open-source baseline, which includes a high-performance <inline-formula><tex-math notation="LaTeX">$\mathrm{SE}(3)$</tex-math></inline-formula> planner and a challenging simulation platform tailored for drone racing. We specify the <inline-formula><tex-math notation="LaTeX">$\mathrm{SE}(3)$</tex-math></inline-formula> trajectory generation as a soft-penalty optimization problem, and speed up the solving process utilizing its underlying parallel structure. Moreover, to provide a testbed for challenging the planner, we develop delicate drone racing tracks which mimic real-world set-up and necessities planning in <inline-formula><tex-math notation="LaTeX">$\mathrm{SE}(3)$</tex-math></inline-formula>. Besides, we provide necessary system components such as common map interfaces and a baseline controller, to make our work plug-in-and-use. With our baseline, we hope to future foster the research of <inline-formula><tex-math notation="LaTeX">$\mathrm{SE}(3)$</tex-math></inline-formula> planning and the competition of autonomous drone racing.