Selected Publications

We present Habitat, a new platform for the development of embodied artificial intelligence (AI). Training robots in the real world is slow, dangerous, expensive, and not easily reproducible. We aim to support a complementary approach – training embodied AI agents (virtual robots) in photorealistic 3D simulation and transferring the learned skills to reality.
We also describe the Habitat Challenge, an autonomous navigation challenge that aims to benchmark and accelerate progress in embodied AI.
arXiv, 2019

This paper presents a novel algorithm that utilizes a 2D floorplan to align panorama RGBD scans. Our approach can significantly reduce the number of necessary scans with the aid of a floorplan image. We formulate a novel Markov Random Field inference problem as a scan placement over the floorplan, as opposed to the conventional scan-to-scan alignment. The technical contributions lie in multi-modal image correspondence cues (between scans and schematic floorplan) as well as a novel coverage potential avoiding an inherent stacking bias. The proposed approach has been evaluated on five challenging large indoor spaces.
CVPR, 2017

Recent Publications

. Habitat: A Platform for Embodied AI Research. arXiv, 2019.


. Building Scale RGBD Alignment. CVPR, 2017.


. (Cross-)Browser Machine Fingerprinting. NDSS, 2017.



Amodal 3D Bounding Box Prediction

During Fall 2017, I worked on using PointNets to predict amodal 3D bounding boxes. I saw initially promising results for regressing bounding boxes coordinates. However, due to my desire to not use fixed anchors, the network had trouble determine which bounding boxes had an object.