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Computer Vision Group
TUM Department of Informatics
Technical University of Munich

Technical University of Munich



Intrinsic3D Dataset

Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting

Robert Maier1,2 Kihwan Kim1 Daniel Cremers2 Jan Kautz1 Matthias Nießner2,3
1NVIDIA 2Technical University of Munich 3Stanford University

IEEE International Conference on Computer Vision (ICCV) 2017

We introduce a novel method to obtain high-quality 3D reconstructions from consumer RGB-D sensors. Our core idea is to simultaneously optimize for geometry encoded in a signed distance field (SDF), textures from automatically-selected keyframes, and their camera poses along with material and scene lighting. To this end, we propose a joint surface reconstruction approach that is based on Shape-from-Shading (SfS) techniques and utilizes the estimation of spatially-varying spherical harmonics (SVSH) from subvolumes of the reconstructed scene. Through extensive examples and evaluations, we demonstrate that our method dramatically increases the level of detail in the reconstructed scene geometry and contributes highly to consistent surface texture recovery.

Intrinsic3D Dataset

The following dataset contains RGB-D sequences and reconstructed 3D models of five different scenes. We captured the RGB-D data using a Structure.io depth sensor (640x480px) and an iPad color camera (1296x968px). Please refer to the respective publication when using this data.


For each scene of the Intrinsic3D dataset, we provide the respective RGB-D sequence as well as the fused and refined 3D models (PLY binary format). Each RGB-D sequence contains:

  • Color frames (frame-XXXXXX.color.png): RGB, 24-bit, PNG
  • Depth frames (frame-XXXXXX.depth.png): depth (mm), 16-bit, PNG (invalid depth is set to 0)
  • Camera poses (frame-XXXXXX.pose.txt): camera-to-world pose computed by VoxelHashing
  • Camera calibration (colorIntrinsics.txt and depthIntrinsics.txt): color and depth camera intrinsics (default factory calibration).

For reference, the RGB-D data is provided in the same format as specified here. The Intrinsic3D source code on github can directly read and process sequences in this format.




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Unless stated otherwise, all data in the Intrinsic3D Dataset is licensed under a Creative Commons 4.0 Attribution License (CC BY 4.0).

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Conference and Workshop Papers
[]Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting (R. Maier, K. Kim, D. Cremers, J. Kautz and M. Niessner), In International Conference on Computer Vision (ICCV), 2017. ([slides] [poster] [dataset] [code]) [bibtex] [pdf]
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In April 2022 Jürgen Sturm and Daniel Cremers were featured among the top 6 most influential scholars in robotics of the last decade.


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We have six papers accepted to CVPR 2022 in New Orleans!


We have two papers accepted to ICRA 2022 - congrats to Lukas von Stumberg, Qing Cheng and Niclas Zeller!