Tanks and temples

By Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, Vladlen Koltun. ACM Transactions on Graphics, 2017

917 citations recorded by the source corpus.

We present a benchmark for image-based 3D reconstruction. The benchmark sequences were acquired outside the lab, in realistic conditions. Ground-truth data was captured using an industrial laser scanner. The benchmark includes both outdoor scenes and indoor environments. High-resolution video sequences are provided as input, supporting the development of novel pipelines that take advantage of video input to increase reconstruction fidelity. We report the performance of many image-based 3D reconstruction pipelines on the new benchmark. The results point to exciting challenges and opportunities for future work.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology

View Paper: https://www.semanticscholar.org/paper/4287fca41f650f9a953a976136d0f9d271ac5c37

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In Aerospace Engineering

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In Geology

Citations in the corpus

3D Gaussian Splatting for Real-Time Radiance Field Rendering (2023, methodology): “…with state-of-the-art (SOTA) visual quality and competitive training times, while our tile-based splatting solution ensures real-time rendering at SOTA quality for 1080p resolution on several previously published datasets [Barron et al. 2022; Hedman et al. 2018; Knapitsch et al. 2017] (see Fig.”
Deep blending for free-viewpoint image-based rendering (2018, methodology): “Recently, Patch-Match based algorithms such as COLMAP [Schönberger et al. 2016] have been demonstrated to create the most accurate geometry in benchmark tests [Knapitsch et al. 2017; Schöps et al. 2017].”
Deep Stereo Using Adaptive Thin Volume Representation With Uncertainty Awareness (2019, neutral): “Evaluation on Tanks and Temple dataset [31].”
2D Gaussian Splatting for Geometrically Accurate Radiance Fields (2024, methodology): “Dataset: We evaluate the performance of our method on various datasets, including DTU [Jensen et al. 2014], Tanks and Tem-ples [Knapitsch et al. 2017], and Mip-NeRF360 [Barron et al. 2022a].”
MAGSAC++, a Fast, Reliable and Accurate Robust Estimator (2019, methodology): “(3) The Tanks and Temples (T&T) dataset [10] provides images of real-world objects for image-based reconstruction and, thus, contains mostly wide-baseline pairs.”
Neural Sparse Voxel Fields (2020, methodology): “• Tanks & Temples.”