Habitat: A Platform for Embodied AI Research
Table 1: Performance of Habitat-Sim in frames per second for an example Matterport3D scene (id 17DRP5sb8fy) on an Intel Xeon E5-2690 v4 CPU and Nvidia Titan Xp GPU, measured at different frame resolutions and with a varying number of concurrent simulator processes sharing the GPU. See the supplement for additional benchmarking results.
['Sensors / Resolution', '1 process 128', '1 process 256', '1 process 512', '5 processes 128', '5 processes 256', '5 processes 512']
Performance. By comparison, AI2-THOR [Kolve2017] and CHALET [Yan2018] run at tens of fps, MINOS [Savva2017] and Gibson [Xia2018] run at about a hundred, and House3D [Wu2018] runs at about 300 fps. Habitat-Sim is 2-3 orders of magnitude faster. By operating at 10@000 frames per second we shift the bottleneck from simulation to optimization for network training. In practice, we have observed that it is often faster to generate images using Habitat-Sim than to load images from disk.