This repository accompanies the VR-NeRF Eyeful Tower dataset (SIGGRAPH Asia 2023), providing the full software stack for capturing, processing, and reconstructing high-quality HDR images from a robotic multi-camera rig. The hardware consists of 14 Sony A1 cameras mounted on a PAL TIAGo mobile base, controlled via a ROS2 node (EyefulController) that orchestrates drive sequences, dual-exposure bracketing, and pose recording. A Godot-based remote control app (Commandly) offers teleoperation, SLAM mapping, capture management, and status monitoring via ROS# and SSH. The processing pipeline includes RAW debayering, HDR merging (PPNE, Debevec, Max), white balance correction, downscaling, JPEG export, and automated Metashape photogrammetry (alignment, dense cloud, mesh, texture) with COLMAP export for NeRF training. The dataset itself (22 indoor scenes, 4.4 TB) is hosted on AWS S3 and includes HDR EXR images at multiple resolutions, camera calibration, and COLMAP reconstructions. While the primary output is a research dataset, the repository contains substantial application-level code: a camera driver library abstracting the Sony Camera Remote SDK, a ROS2 sensor validation node (IMU, LiDAR), and a full-featured Godot UI. These components are tightly coupled to the specific hardware (Sony A1, PAL TIAGo, Ouster LiDAR, Xsens IMU) and proprietary dependencies (Sony SDK, Metashape, LibRaw). The pipeline scripts are parameterized and can be run individually, but require manual environment setup and specific directory structures. The code is functional and achieves its research goals, but is not designed for general reuse without significant adaptation.