Tanks and Temples: Benchmarking Large-Scale Scene Reconstruction
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Latest comments
by Jamie Gibson
11 hours ago

Hi Elliott, This is a great breakdown and very generous in sharing your process and insights, you came a long way from the vending machine days!

Are you planning on releasing the UE4 project to the public? Or only builds? I'd love to play around with it in the editor if possible!

by mr. Awesome
16 hours ago

Fucking AWESOME!

Tanks and Temples: Benchmarking Large-Scale Scene Reconstruction
24 May, 2017
News
Existing reconstruction techniques have significant limitations. For example, take a camera, walk around a building recording a video, and use that data to create a 3D model. You won’t get a clean and accurate reconstruction of the building. Or walk around a house with your camera and try reconstructing it. The result will definitely upset you. Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun from Intel Labs presented a new benchmark for image-based 3D reconstruction.

The researchers used a state-of-the-art industrial laser scanner with a range of 330 meters and submillimeter accuracy to acquire ground-truth models of large-scale scenes. They’ve scanned objects and environments from multiple viewpoints and registered the scans to obtain ground-truth models. Then, they used 8-megapixel video to reconstruct models.

The presented benchmark has a number of characteristics that can support the development of new reconstruction techniques:

  • The input modality is video. This can help future pipelines track the camera, reason about illumination and reflectance, and reconstruct small details.
  • The benchmark evaluates complete reconstruction pipelines. This leaves scope for tackling camera localization and dense reconstruction jointly, potentially increasing robustness and precision via co-adaptation to the performance characteristics of each task.
  • The benchmark includes both outdoor and indoor scans of complete scenes, pushing current reconstruction pipelines to their limits and beyond.

Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun

This research can drastically change the way developers reconstruct outdoor and indoor scenes and  stimulate the development of robust broad-competence systems. The researchers will set up an evaluation server and online leaderboard that can be used by the community to track progress here.

You can find the full paper on the new benchmark here. And here is the link with the input data for all the benchmark sequences.

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