DeepBillboards

Naruya Kondo, So Kuroki, Ryosuke Hyakuta, Yutaka Matsuo, Shixiang Shane Gu, Yoichi Ochiai

University of Tsukuba, University of Tokyo, Google Brain

SIGGRAPH 2022 Immersive Pavilion

(Video: Kengo Tanaka & Takumi Yokoyama)

In this demo, *5* models (512x512) are running at 20~30 fps on a single GPU (RTX 3090)
3 models (800x800) : > 30 fps
1 model (800x800) : > 100 fps
Cloud rendering is also supported (check our paper!)

These models are automatically generated from images in 5~10 minutes (training of plenoxels and conversion to plenoctrees). 

Abstract

An aspirational goal for virtual reality (VR) is to bring in a rich diversity of real world objects losslessly. Existing VR applications often convert objects into explicit 3D models with meshes or point clouds, which allow fast interactive rendering but also severely limit its quality and the types of supported objects, fundamentally upper-bounding the "realism" of VR. Inspired by the classic "billboards" technique in gaming, we develop Deep Billboards that model 3D objects implicitly using neural networks, where only 2D image is rendered at a time based on the user’s viewing direction. Our system, connecting a commercial VR headset with a server running neural rendering, allows real-time high-resolution simulation of detailed rigid objects, hairy objects, actuated dynamic objects and more in an interactive VR world, drastically narrowing the existing real-to-simulation (real2sim) gap. Additionally, we augment Deep Billboards with physical interaction capability, adapting classic billboards from screen-based games to immersive VR.  At our pavilion, the visitors can use our off-the-shelf setup for quickly capturing their favorite objects, and within minutes, experience them in an immersive and interactive VR world – with minimal loss of reality.

This work was supported by the Strategic Information and Communications R&D Promotion Programme (SCOPE) of the Ministry of Internal Affairs and Communications of Japan and New Energy and Industrial Technology Development Organization (NEDO) of Ministry of Economy, Trade and Industry of Japan.

Special Thanks to shi3z-san

この研究は経産省未踏AIと総務省異能vationプログラムの支援を受けています





Appendix