Angela Dai

3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans

Ji Hou, Angela Dai, Matthias Nießner

CVPR 2019 (Oral)

We introduce 3D-SIS, a novel neural network architecture for 3D semantic instance segmentation in commodity RGB-D scans.

Scan2CAD: Learning CAD Model Alignment in RGB-D Scans

Armen Avetisyan, Manuel Dahnert, Angela Dai, Angel X. Chang, Manolis Savva, Matthias Nießner

CVPR 2019 (Oral)

We present Scan2CAD, a novel data-driven method that learns to align clean 3D CAD models from a shape database to the noisy and incomplete geometry of an RGB-D scan.

Scan2Mesh: From Unstructured Range Scans to 3D Meshes

Angela Dai, Matthias Nießner

CVPR 2019

We introduce Scan2Mesh, a novel data-driven approach which introduces a generative neural network architecture for creating 3D meshes as indexed face sets, conditioned on an input partial scan.

3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation

Angela Dai, Matthias Nießner

ECCV 2018

We present 3DMV, a novel method for 3D semantic scene segmentation of RGB-D scans in indoor environments using a joint 3D-multi-view prediction network. In contrast to existing methods that either use geometry or RGB data as input for this task, we combine both data modalities in a joint, end-to-end network architecture.

ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans

Angela Dai, Daniel Ritchie, Martin Bokeloh, Scott Reed, Jürgen Sturm, Matthias Nießner

CVPR 2018

We introduce ScanComplete, a novel data-driven approach for taking an incomplete 3D scan of a scene as input and predicting a complete 3D model along with per-voxel semantic labels. The key contribution of our method is its ability to handle large scenes with varying spatial extent, managing the cubic growth in data size as scene size increases.

Matterport3D: Learning from RGB-D Data in Indoor Environments

Angel X. Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Nießner, Manolis Savva, Shuran Song, Andy Zeng, Yinda Zhang

3DV 2017

In this paper, we introduce Matterport3D, a large-scale RGB-D dataset containing 10,800 panoramic views from 194,400 RGB-D images of 90 building-scale scenes.

3DLite: Towards Commodity 3D Scanning for Content Creation

Jingwei Huang, Angela Dai, Leonidas Guibas, Matthias Nießner

ACM Transactions on Graphics 2017 (TOG)

We present 3DLite, a novel approach to reconstruct 3D environments using consumer RGB-D sensors, making a step towards directly utilizing captured 3D content in graphics applications, such as video games, VR, or AR. Rather than reconstructing an accurate one-to-one representation of the real world, our method computes a lightweight, low-polygonal geometric abstraction of the scanned geometry.

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.

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.

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.

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.

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.

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.

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.