526 citations · 870 across the 3 of their papers we have counts for
5 papers
Rescan: Inductive Instance Segmentation for Indoor RGBD Scans
Maciej Halber, Yifei Shi, Kai Xu +1
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 re…
Accelerating Large-Kernel Convolution Using Summed-Area Tables
Linguang Zhang, Maciej Halber, Szymon Rusinkiewicz
Expanding the receptive field to capture large-scale context is key to obtaining good performance in dense prediction tasks, such as human pose estimation. While many state-of-the-…
Matterport3D: Learning from RGB-D Data in Indoor Environments
Angel Chang, Angela Dai, Thomas Funkhouser +6
Access to large, diverse RGB-D datasets is critical for training RGB-D scene understanding algorithms. However, existing datasets still cover only a limited number of views or a re…
ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
Angela Dai, Angel X. Chang, Manolis Savva +3
A key requirement for leveraging supervised deep learning methods is the availability of large, labeled datasets. Unfortunately, in the context of RGB-D scene understanding, very l…
Fine-To-Coarse Global Registration of RGB-D Scans
Maciej Halber, Thomas Funkhouser
RGB-D scanning of indoor environments is important for many applications, including real estate, interior design, and virtual reality. However, it is still challenging to register…