Download

---

Our benchmark dataset is designed to evaluate robust multi-view geometry and novel view synthesis algorithms in a variety of conditions. The full dataset is split into two main archives:

• val.zip – Contains all validation data
• test.zip – Contains all test sequences

Dataset Categories

The dataset includes three main types of scenes, grouped under the top-level Benchmark/ folder:

• Object Scanning (285 videos): Videos focus on individual objects across a wide variety of indoor and outdoor environments, lighting conditions, and camera trajectories. These include static and dynamic scenes, non-Lambertian surfaces, and texture-less backgrounds.
• Indoor Scenes (40 videos): Long trajectories filmed in indoor environments, including transitions between indoor and outdoor. These challenging sequences contain textureless regions, dynamic elements, and challenging lighting.
• Outdoor Scenes (40 videos): Diverse dynamic environments filmed in unique outdoor locations. These include different weather and lighting conditions such as full sunlight and nighttime.

File Structure

Inside each of the scanning/, indoors/, and outdoors/ folders, the data is further divided into subfolders:

• part_1/, part_2/, ..., part_N/: Each part contains one or more video sequences captured with the same rotation of the 360° user camera. Intrinsic parameters for the 360 camera view are provided per part.

Additional Downloads

• Stereo & IMU Data: For some video, we provide synchronized stereo and inertial data. Each download contains the following structure:
  • LEFT/ – Left camera images
  • RIGHT/ – Right camera images
  • imu/ – Inertial measurement unit (IMU) readings

  - stereo-val.zip (Coming on Monday 6/22/25)
  - stereo-test.zip (Coming on Monday 6/22/25)

NVS Benchmark

We also propose a challenging benchmark for Novel View Synthesis (NVS) based on six sequences selected from our Princeton365 dataset. These scenes involve complete 360° camera rotations around reflective or non-Lambertian objects—ideal for evaluating methods that aim to reconstruct challenging materials under diverse lighting.

The NVS benchmark data and evaluation trajectories can be downloaded here:
NVS Benchmark Sequences

For any questions or assistance, please contact us at: princeton365.benchmark@gmail.com