Publications 2017




Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis

Angela Dai, Charles Ruizhongtai Qi, Matthias Nießner

CVPR 2017 (Spotlight)

We introduce a data-driven approach to complete partial 3D shapes through a combination of volumetric deep neural networks and 3D shape synthesis. From a partially-scanned input shape, our method first infers a low-resolution -- but complete -- output.

[paper] [bibtex] [project page]

ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes

Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner

CVPR 2017 (Spotlight)

We introduce ScanNet, an RGB-D video dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations.

[paper] [bibtex] [project page]

BundleFusion: Real-time Globally Consistent 3D Reconstruction using On-the-fly Surface Re-integration

Angela Dai, Matthias Nießner, Michael Zollhöfer, Shahram Izadi, Christian Theobalt

ACM Transactions on Graphics 2017 (TOG)

We introduce a novel, real-time, end-to-end 3D reconstruction framework, with a robust pose optimization strategy based on sparse feature matches and dense geometric and photometric alignment. One main contribution is the ability to update the reconstructed model on-the-fly as new (global) pose optimization results become available.

[paper] [video] [bibtex] [project page]


Publications 2016




Volumetric and Multi-View CNNs for Object Classification on 3D Data

Charles Ruizhongtai Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, Leonidas Guibas

CVPR 2016 (Spotlight)

In this paper, we improve both Volumetric CNNs and Multi-view CNNs by introducing new distinct network architectures. Overall, we are able to outperform current state-of-the-art methods for both Volumetric CNNs and Multi-view CNNs.

[paper] [bibtex] [project page]


Publications 2015




Shading-based Refinement on Volumetric Signed Distance Functions

Michael Zollhöfer, Angela Dai, Matthias Innmann, Chenglei Wu, Marc Stamminger, Christian Theobalt, Matthias Nießner

ACM Transactions on Graphics 2015 (TOG)

We present a novel method to obtain fine-scale detail in 3D reconstructions generated with low-budget RGB-D cameras or other commodity scanning devices.

[paper] [video] [bibtex] [project page]

Database-Assisted Object Retrieval for Real-Time 3D Reconstruction

Yangyan Li, Angela Dai, Leonidas Guibas, Matthias Nießner

Eurographics 2015

We present a novel reconstruction approach based on retrieving objects from a 3D shape database while scanning an environment in real-time. With this approach, we are able to replace scanned RGB-D data with complete, hand-modeled objects from shape databases.

[paper] [video] [bibtex] [project page]


Publications 2014




Combining Inertial Navigation and ICP for Real-time 3D Surface Reconstruction

Matthias Nießner, Angela Dai, Matthew Fisher

Eurographics 2014

We present a novel method to improve the robustness of real-time 3D surface reconstruction by incorporating inertial sensor data when determining inter-frame alignment. With commodity inertial sensors, we can significantly reduce the number of iterative closest point (ICP) iterations required per frame.

[paper] [video] [bibtex] [project page]