This report serves as a supplementary document for TaskPrompter, detailing
its implementation on a new joint 2D-3D multi-task learning benchmark based on
Cityscapes-3D. TaskPrompter presents an innovative multi-task prompting
framework that unifies the learning of (i) task-generic representations, (ii)
task-specific representations, and (iii) cross-task interactions, as opposed to
previous approaches that separate these learning objectives into different
network modules. This unified approach not only reduces the need for meticulous
empirical structure design but also significantly enhances the multi-task
network's representation learning capability, as the entire model capacity is
devoted to optimizing the three objectives simultaneously. TaskPrompter
introduces a new multi-task benchmark based on Cityscapes-3D dataset, which
requires the multi-task model to concurrently generate predictions for
monocular 3D vehicle detection, semantic segmentation, and monocular depth
estimation. These tasks are essential for achieving a joint 2D-3D understanding
of visual scenes, particularly in the development of autonomous driving
systems. On this challenging benchmark, our multi-task model demonstrates
strong performance compared to single-task state-of-the-art methods and
establishes new state-of-the-art results on the challenging 3D detection and
depth estimation tasks.

TaskPrompter 是一种多任务提示框架，通过使用 Cityscapes-3D 数据集实现联合 2D-3D 任务学习，以实现单眼 3D 车辆检测、语义分割和单眼深度估计的预测，为开发自动驾驶系统做出了积极的贡献，相较于单任务最先进方法表现出更强的性能，并在挑战性的 3D 检测和深度估计任务上建立了新的最先进结果。