Neural radiance fields (NeRF) achieve impressive performance in novel view synthesis
when trained on only single sequence data. However, leveraging multiple sequences …

Abstract Neural Radiance Fields (NeRF) have emerged as a powerful representation for the
task of novel view synthesis due to their simplicity and state-of-the-art performance. Though …

Multi-task visual learning is a critical aspect of computer vision. Current research, however,
predominantly concentrates on the multi-task dense prediction setting, which overlooks the …

Abstract Neural Radiance Field (NeRF) has broken new ground in the novel view synthesis
due to its simple concept and state-of-the-art quality. However, it suffers from severe …

Although neural radiance fields (NeRF) have shown impressive advances in novel view
synthesis, most methods require multiple input images of the same scene with accurate …

We propose im2nerf, a learning framework that predicts a continuous neural object
representation given a single input image in the wild, supervised by only segmentation …

Abstract Neural Radiance Fields (NeRF) has achieved unprecedented view synthesis
quality using coordinate-based neural scene representations. However, NeRF's view …

Neural Radiance Fields (NeRF) achieve impressive view synthesis results for a variety of
capture settings, including 360 capture of bounded scenes and forward-facing capture of …

We present NeRF-SR, a solution for high-resolution (HR) novel view synthesis with mostly
low-resolution (LR) inputs. Our method is built upon Neural Radiance Fields (NeRF) that …

Abstract Neural Radiance Field (NeRF) has shown impressive performance in novel view
synthesis via implicit scene representation. However, it usually suffers from poor scalability …