面向配备轻量型ToF传感器的单目稠密SLAM的多模态神经辐射场
Multi-Modal Neural Radiance Field for Monocular Dense SLAM with a Light-Weight ToF Sensor
AI总结：
本研究提出首个结合单目相机与轻量型ToF传感器的稠密SLAM系统，通过多模态隐式场景表示、中间深度监督、粗到细优化及时序信息处理，提升了位姿跟踪与稠密重建的效果。
摘要：
轻量型飞行时间（ToF）深度传感器结构紧凑、成本效益高，因此被广泛应用于移动设备的自动对焦、障碍物检测等任务中。但由于其深度测量结果稀疏且含噪声，这类传感器极少被用于稠密几何重建。本研究提出了首个采用单目相机与轻量型ToF传感器的稠密SLAM系统。具体而言，我们提出了一种多模态隐式场景表示方法，该方法支持渲染来自RGB相机与轻量型ToF传感器的信号，通过与原始传感器输入对比来驱动优化过程。此外，为保障位姿跟踪与重建的顺利进行，我们利用预测深度作为中间监督，并设计了由粗到细的优化策略，以高效学习隐式表示。最后，我们显式利用时序信息处理轻量型ToF传感器的噪声信号，提升系统的准确性与鲁棒性。实验表明，本系统可有效利用轻量型ToF传感器的信号，在相机跟踪与稠密场景重建任务上均取得了具有竞争力的结果。项目页面：https://zju3dv.github.io/tof_slam/。
英文摘要：
Light-weight time-of-flight (ToF) depth sensors are compact and cost-efficient, and thus widely used on mobile devices for tasks such as autofocus and obstacle detection. However, due to the sparse and noisy depth measurements, these sensors have rarely been considered for dense geometry reconstruction. In this work, we present the first dense SLAM system with a monocular camera and a light-weight ToF sensor. Specifically, we propose a multi-modal implicit scene representation that supports rendering both the signals from the RGB camera and light-weight ToF sensor which drives the optimization by comparing with the raw sensor inputs. Moreover, in order to guarantee successful pose tracking and reconstruction, we exploit a predicted depth as an intermediate supervision and develop a coarse-to-fine optimization strategy for efficient learning of the implicit representation. At last, the temporal information is explicitly exploited to deal with the noisy signals from light-weight ToF sensors to improve the accuracy and robustness of the system. Experiments demonstrate that our system well exploits the signals of light-weight ToF sensors and achieves competitive results both on camera tracking and dense scene reconstruction. Project page: \url{https://zju3dv.github.io/tof_slam/}.
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。
