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SparseGNV: Generating Novel Views of Indoor Scenes with Sparse Input Views

SparseGNV generates novel view images of indoor scenes based on 4 observed views:

[ArXiv Paper]

NOTE: This code repository provides a streamlined version of SparseGNV. Specifically, the neural geometry module originally designed with Point-NeRF is modified to using raw point clouds with point-based renderer, as the code based on Point-NeRF is overly intricate for integration.

TL;DR

  • SparseGNV generates novel views of indoor scenes given sparse RGB-D input views.
  • SparseGNV consists of three modules: 1) a neural point cloud (Point-NeRF) built from input views to project visual guidance; 2) an autoregressive transformer to generate image tokens of the target view conditioned on visual guidance; 3) a VQ decoder to recover the RGB image given the generated image tokens.

Setup Environment

Step 1: Create a python environment using Anaconda or Miniconda:

conda create -n sparsegnv python=3.10
conda activate sparsegnv

Step 2: Install PyTorch3D (including PyTorch) following Official Instructions.

Step 3: Install additional packages:

pip install -r requirements.txt

Download Checkpoints

Download the VQ decoder model and the generator model. Put the checkpoints under ckpts/.

Run Demo

The demo is tested on an NVIDIA Tesla V100 32G GPU.

Step 1: Render hints with sparse RGB-D inputs and novel camera poses:

python render_hint.py --data_root data/scene0710_00

Step 2: Generate novel view images:

python generate.py --data_root data/scene0710_00

Prepare your own data

The organization of the testing data follows ScanNet. We provide an example in data/scene0710_00/.

  • The captured data are placed under exported/ with color images, depth images, camera intrinsics, and camera poses (cam2world matrix in the Blender format).
  • The poses of targeting novel views are placed under novel_pose/.
  • obs_vids.txt provides the frame names of the observed images and poses.
  • novel_vids.txt provides the frame names of the novel poses.

Please make sure that all the frame names are numbers and unique.

Citation

@article{cheng2023sparsegnv,
  title={SparseGNV: Generating Novel Views of Indoor Scenes with Sparse Input Views},
  author={Cheng, Weihao and Cao, Yan-Pei and Shan, Ying},
  journal={arXiv preprint arXiv:2305.07024},
  year={2023}
}