ASSIST: Interactive Scene Nodes for Scalable and Realistic Indoor
  Simulation
Zhide Zhong Jiakai Cao Songen Gu Sirui Xie Weibo Gao Liyi Luo Zike Yan Hao Zhao Guyue Zhou
We present ASSIST, an object-wise neural radiance field as a panoptic
representation for compositional and realistic simulation. Central to our
approach is a novel scene node data structure that stores the information of
each object in a unified fashion, allowing online interaction in both intra-
and cross-scene settings. By incorporating a differentiable neural network
along with the associated bounding box and semantic features, the proposed
structure guarantees user-friendly interaction on independent objects to scale
up novel view simulation. Objects in the scene can be queried, added,
duplicated, deleted, transformed, or swapped simply through mouse/keyboard
controls or language instructions. Experiments demonstrate the efficacy of the
proposed method, where scaled realistic simulation can be achieved through
interactive editing and compositional rendering, with color images, depth
images, and panoptic segmentation masks generated in a 3D consistent manner.