activity
20162024
most citedMatterport3D: Learning from RGB-D Data in Indoor Environments

337 citations · 1.3k across the 62 of their papers we have counts for

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Showing 2020Show all

23 papers · 1 filter

cs.CV2020

Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D Sequences

Norman Müller, Yu-Shiang Wong, Niloy J. Mitra +2

Multi-object tracking from RGB-D video sequences is a challenging problem due to the combination of changing viewpoints, motion, and occlusions over time. We observe that having th…

cs.CV2020★ 1 cited

Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction

Guy Gafni, Justus Thies, Michael Zollhöfer +1

We present dynamic neural radiance fields for modeling the appearance and dynamics of a human face. Digitally modeling and reconstructing a talking human is a key building-block fo…

cs.CV2020

Scan2Cap: Context-aware Dense Captioning in RGB-D Scans

Dave Zhenyu Chen, Ali Gholami, Matthias Nießner +1

We introduce the task of dense captioning in 3D scans from commodity RGB-D sensors. As input, we assume a point cloud of a 3D scene; the expected output is the bounding boxes along…

cs.CV2020

Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction

Aljaž Božič, Pablo Palafox, Michael Zollhöfer +3

We introduce Neural Deformation Graphs for globally-consistent deformation tracking and 3D reconstruction of non-rigid objects. Specifically, we implicitly model a deformation grap…

cs.CV2020★ 15 cited

SceneFormer: Indoor Scene Generation with Transformers

Xinpeng Wang, Chandan Yeshwanth, Matthias Nießner

We address the task of indoor scene generation by generating a sequence of objects, along with their locations and orientations conditioned on a room layout. Large-scale indoor sce…

cs.CV2020

Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts

Ji Hou, Benjamin Graham, Matthias Nießner +1

The rapid progress in 3D scene understanding has come with growing demand for data; however, collecting and annotating 3D scenes (e.g. point clouds) are notoriously hard. For examp…