稀疏胜过密集：重新思考雷达-相机深度补全中的监督方式
Sparse Beats Dense: Rethinking Supervision in Radar-Camera Depth Completion
AI总结：
针对深度补全中稀疏监督劣于密集监督的普遍认知，提出干扰-补偿框架抑制激光雷达分布泄漏，使稀疏监督在MAE和速度上超越密集监督。
摘要：
在深度补全领域，人们普遍认为稀疏监督不如密集监督，但对此背后的原因却鲜有讨论。为此，我们重新审视雷达-相机深度补全任务，并提出一种采用稀疏激光雷达监督的新方法，在精度和速度上均优于先前采用密集激光雷达监督的方法。具体而言，当使用稀疏激光雷达监督训练时，深度补全模型通常输出含有显著条状伪影的深度图。我们发现，这种现象是由稀疏激光雷达监督隐式学习到的位置分布模式所致，本文将其称为激光雷达分布泄漏（LiDAR Distribution Leakage, LDL）。基于这一理解，我们提出一种新颖的干扰-补偿（Disruption-Compensation）雷达-相机深度补全框架来解决该问题。干扰部分旨在有意干扰从稀疏监督中学习激光雷达分布的过程，而补偿部分旨在利用3D空间和2D语义信息来补偿先前干扰造成的信息损失。大量实验结果表明，通过减少LDL的影响，我们的框架在稀疏监督下优于最先进的密集监督方法，平均绝对误差（Mean Absolute Error, MAE）改善11.6%，帧率（Frame Per Second, FPS）提升1.6倍。代码可在 https://github.com/megvii-research/Sparse-Beats-Dense 获取。
英文摘要：
It is widely believed that sparse supervision is worse than dense supervision in the field of depth completion, but the underlying reasons for this are rarely discussed. To this end, we revisit the task of radar-camera depth completion and present a new method with sparse LiDAR supervision to outperform previous dense LiDAR supervision methods in both accuracy and speed. Specifically, when trained by sparse LiDAR supervision, depth completion models usually output depth maps containing significant stripe-like artifacts. We find that such a phenomenon is caused by the implicitly learned positional distribution pattern from sparse LiDAR supervision, termed as LiDAR Distribution Leakage (LDL) in this paper. Based on such understanding, we present a novel Disruption-Compensation radar-camera depth completion framework to address this issue. The Disruption part aims to deliberately disrupt the learning of LiDAR distribution from sparse supervision, while the Compensation part aims to leverage 3D spatial and 2D semantic information to compensate for the information loss of previous disruptions. Extensive experimental results demonstrate that by reducing the impact of LDL, our framework with sparse supervision outperforms the state-of-the-art dense supervision methods with 11.6% improvement in Mean Absolute Error (MAE)} and 1.6x speedup in Frame Per Second (FPS)}. The code is available at https://github.com/megvii-research/Sparse-Beats-Dense.
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。
