WHU-Synthetic：面向3D多任务模型研究的合成感知数据集
WHU-Synthetic: A Synthetic Perception Dataset for 3-D Multitask Model Research
AI总结：
本文提出WHU-Synthetic，一个大规模3D合成感知数据集，支持从数据增强到场景理解及宏观任务的多任务学习，通过内在对齐和新型设置促进多任务模型的构建与互惠研究。
摘要：
能够并行处理多个子任务的端到端模型已成为一种新趋势，这为3D视觉领域内多任务的集成带来了重大挑战和机遇。3D数据采集条件的局限性不仅限制了许多创新研究问题的探索，也导致现有的3D数据集主要聚焦于单一任务。这导致了3D多任务学习缺乏系统的方法和理论框架，大多数工作仅作为主任务的辅助支持。在本文中，我们介绍了WHU-Synthetic，一个专为多任务学习设计的大规模3D合成感知数据集，涵盖从初始数据增强（上采样和深度补全），到场景理解（分割），再到宏观任务（位置识别和3D重建）。由于在同一环境域中采集，我们确保了子任务之间的内在对齐，从而无需单独的训练方法即可构建多任务模型。此外，我们实现了若干新颖的设置，使得在真实场景中难以实现的某些想法成为可能。这支持了更具适应性和鲁棒性的多任务感知任务，例如在城市级模型上进行采样、提供不同密度的点云以及模拟时间变化。利用我们的数据集，我们进行了多项实验以探究子任务之间的互惠益处，揭示了新的观察、挑战和未来研究的机会。该数据集可在https://github.com/WHU-USI3DV/WHU-Synthetic获取。
英文摘要：
End-to-end models capable of handling multiple sub-tasks in parallel have become a new trend, thereby presenting significant challenges and opportunities for the integration of multiple tasks within the domain of 3D vision. The limitations of 3D data acquisition conditions have not only restricted the exploration of many innovative research problems but have also caused existing 3D datasets to predominantly focus on single tasks. This has resulted in a lack of systematic approaches and theoretical frameworks for 3D multi-task learning, with most efforts merely serving as auxiliary support to the primary task. In this paper, we introduce WHU-Synthetic, a large-scale 3D synthetic perception dataset designed for multi-task learning, from the initial data augmentation (upsampling and depth completion), through scene understanding (segmentation), to macro-level tasks (place recognition and 3D reconstruction). Collected in the same environmental domain, we ensure inherent alignment across sub-tasks to construct multi-task models without separate training methods. Besides, we implement several novel settings, making it possible to realize certain ideas that are difficult to achieve in real-world scenarios. This supports more adaptive and robust multi-task perception tasks, such as sampling on city-level models, providing point clouds with different densities, and simulating temporal changes. Using our dataset, we conduct several experiments to investigate mutual benefits between sub-tasks, revealing new observations, challenges, and opportunities for future research. The dataset is accessible at https://github.com/WHU-USI3DV/WHU-Synthetic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
