no code implementations • ICCV 2019 • Maciej Halber, Yifei Shi, Kai Xu, Thomas Funkhouser
In depth-sensing applications ranging from home robotics to AR/VR, it will be common to acquire 3D scans of interior spaces repeatedly at sparse time intervals (e. g., as part of regular daily use).
no code implementations • 26 Jun 2019 • Linguang Zhang, Maciej Halber, Szymon Rusinkiewicz
In this work, we explore using learnable box filters to allow for convolution with arbitrarily large kernel size, while keeping the number of parameters per filter constant.
1 code implementation • 18 Sep 2017 • Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Nießner, Manolis Savva, Shuran Song, Andy Zeng, yinda zhang
Access to large, diverse RGB-D datasets is critical for training RGB-D scene understanding algorithms.
1 code implementation • CVPR 2017 • Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner
A key requirement for leveraging supervised deep learning methods is the availability of large, labeled datasets.
Ranked #11 on Semantic Segmentation on ScanNetV2
no code implementations • CVPR 2017 • Maciej Halber, Thomas Funkhouser
RGB-D scanning of indoor environments is important for many applications, including real estate, interior design, and virtual reality.