SLAM&Render: 一个用于神经渲染、高斯点绘和SLAM交叉领域的基准数据集
SLAM&Render: A Benchmark for the Intersection Between Neural Rendering, Gaussian Splatting and SLAM
AI总结：
本文提出SLAM&Render数据集，用于评估神经渲染和SLAM在多模态、多视角和光照条件下的交叉方法，包含40个序列的RGB-D图像、IMU数据和机器人运动数据，验证了该数据集在新兴研究领域的相关性。
摘要：
为新型视图合成和场景渲染开发的模型和方法，如神经辐射场（NeRF）和高斯点绘，越来越多地被用作同时定位与建图（SLAM）中的表示。然而，现有数据集未能涵盖两个领域的特定挑战，如顺序操作以及在许多情况下SLAM中的多模态或神经渲染中的视角和光照条件泛化。此外，数据通常由手持或安装在无人机或移动机器人上的传感器收集，这会复杂化传感器运动的准确再现。为弥合这些差距，我们介绍了SLAM&Render，一个新颖的数据集，用于在SLAM、新型视图渲染和高斯点绘的交叉领域评估方法。该数据集由机器人机械臂记录，唯一包含40个序列的同步RGB-D图像、IMU读数、机器人运动学数据和真实姿态流。通过释放机器人运动学数据，该数据集还允许评估最近的SLAM范式在机器人应用中的整合。该数据集包含五个设置，其中消费者和工业物体在四种受控光照条件下，每个设置都有独立的训练和测试轨迹。所有序列都是静态的，具有不同级别的物体重排和遮挡。我们的实验结果，通过文献中的几个基线验证了SLAM&Render作为该新兴研究领域相关基准的适用性。
英文摘要：
Models and methods originally developed for Novel View Synthesis and Scene Rendering, such as Neural Radiance Fields (NeRF) and Gaussian Splatting, are increasingly being adopted as representations in Simultaneous Localization and Mapping (SLAM). However, existing datasets fail to include the specific challenges of both fields, such as sequential operations and, in many settings, multi-modality in SLAM or generalization across viewpoints and illumination conditions in neural rendering. Additionally, the data are often collected using sensors which are handheld or mounted on drones or mobile robots, which complicates the accurate reproduction of sensor motions. To bridge these gaps, we introduce SLAM&Render, a novel dataset designed to benchmark methods in the intersection between SLAM, Novel View Rendering and Gaussian Splatting. Recorded with a robot manipulator, it uniquely includes 40 sequences with time-synchronized RGB-D images, IMU readings, robot kinematic data, and ground-truth pose streams. By releasing robot kinematic data, the dataset also enables the assessment of recent integrations of SLAM paradigms within robotic applications. The dataset features five setups with consumer and industrial objects under four controlled lighting conditions, each with separate training and test trajectories. All sequences are static with different levels of object rearrangements and occlusions. Our experimental results, obtained with several baselines from the literature, validate SLAM&Render as a relevant benchmark for this emerging research area.
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。
