PixelNeRF is a learning framework that predicts a Neural Radiance Field (NeRF) representation from a single or few input images. Unlike traditional NeRF, which requires extensive optimization and multiple calibrated views, PixelNeRF uses a fully convolutional approach to condition the NeRF on spatial image features, allowing it to be trained on multi-view images and perform novel view synthesis from sparse input views. The model is trained directly from images without explicit 3D supervision, leveraging volume rendering techniques. Extensive experiments on ShapeNet benchmarks and real datasets demonstrate PixelNeRF's superior performance in novel view synthesis and single-image 3D reconstruction compared to state-of-the-art baselines. PixelNeRF can handle category-specific and category-agnostic tasks, as well as unseen object categories and complex scenes, making it a flexible and efficient solution for 3D scene reconstruction.PixelNeRF is a learning framework that predicts a Neural Radiance Field (NeRF) representation from a single or few input images. Unlike traditional NeRF, which requires extensive optimization and multiple calibrated views, PixelNeRF uses a fully convolutional approach to condition the NeRF on spatial image features, allowing it to be trained on multi-view images and perform novel view synthesis from sparse input views. The model is trained directly from images without explicit 3D supervision, leveraging volume rendering techniques. Extensive experiments on ShapeNet benchmarks and real datasets demonstrate PixelNeRF's superior performance in novel view synthesis and single-image 3D reconstruction compared to state-of-the-art baselines. PixelNeRF can handle category-specific and category-agnostic tasks, as well as unseen object categories and complex scenes, making it a flexible and efficient solution for 3D scene reconstruction.