Authors
Alex Yu, Vickie Ye, Matthew Tancik, Angjoo Kanazawa
UC Berkeley
Portals
Summary
Leveraging the volume rendering approach of NeRF, our model can be trained directly from images with no explicit 3D supervision. We conduct extensive experiments on ShapeNet benchmarks for single image novel view synthesis tasks with held-out objects as well as entire unseen categories. We further demonstrate the flexibility of pixelNeRF by demonstrating it on multi-object ShapeNet scenes and real scenes from the DTU dataset. In all cases, pixelNeRF outperforms current state-of-the-art baselines for novel view synthesis and single image 3D reconstruction.
Abstract
We propose pixelNeRF, a learning framework that predicts a continuous neural scene representation conditioned on one or few input images. The existing approach for constructing neural radiance fields involves optimizing the representation to every scene independently, requiring many calibrated views and significant compute time. We take a step towards resolving these shortcomings by introducing an architecture that conditions a NeRF on image inputs in a fully convolutional manner. This allows the network to be trained across multiple scenes to learn a scene prior, enabling it to perform novel view synthesis in a feed-forward manner from a sparse set of views (as few as one). Leveraging the volume rendering approach of NeRF, our model can be trained directly from images with no explicit 3D supervision. We conduct extensive experiments on ShapeNet benchmarks for single image novel view synthesis tasks with held-out objects as well as entire unseen categories. We further demonstrate the flexibility of pixelNeRF by demonstrating it on multi-object ShapeNet scenes and real scenes from the DTU dataset. In all cases, pixelNeRF outperforms current state-of-the-art baselines for novel view synthesis and single image 3D reconstruction. For the video and code, please visit the project website: https://alexyu.net/pixelnerf
Related Works
Novel View Synthesis; Learning-based 3D reconstruction; Viewer-centric 3D reconstruction
Comparisons
SRN, DVR, NeRF, GRF, TCO, dGQN, ENR, SoftRas
Overview
For a query point x along a target camera ray with view direction d, a corresponding image feature is extracted from the feature volume W via projection and interpolation. This feature is then passed into the NeRF network f along with the spatial coordinates. The output RGB and density value is volume-rendered and compared with the target pixel value. The coordinates x and d are in the camera coordinate system of the input view