2021/03/27 by Jiaxin Li, Li, Jiaxin, Zijian Feng +9 · 11 citations
Computer Science · #Advanced Image Processing Techniques #Advanced Vision and Imaging #Computer Graphics and Visualization Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Graphics (cs.GR) #Machine Learning (cs.LG) #cs.CV #cs.GR #cs.LG
paper · pdf · doi:10.48550/arxiv.2103.14910
ICCV 2021. Main paper and supplementary materials
openalex publication_date 2021/03/27 · arxiv created 2021/07/30 · arxiv updated 2021/08/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we propose MINE to perform novel view synthesis and depth estimation via dense 3D reconstruction from a single image. Our approach is a continuous depth generalization of the Multiplane Images (MPI) by introducing the NEural radiance fields (NeRF). Given a single image as input, MINE predicts a 4-channel image (RGB and volume density) at arbitrary depth values to jointly reconstruct the camera frustum and fill in occluded contents. The reconstructed and inpainted frustum can then be easily rendered into novel RGB or depth views using differentiable rendering. Extensive experiments on RealEstate10K, KITTI and Flowers Light Fields show that our MINE outperforms state-of-the-art by a large margin in novel view synthesis. We also achieve competitive results in depth estimation on iBims-1 and NYU-v2 without annotated depth supervision. Our source code is available at https://github.com/vincentfung13/MINE