im2nerf: Learning Continuous Neural Representations from Single View Images

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Computer Vision

im2nerf: Learning Continuous Neural Representations from Single View Images

im2nerf: Image to Neural Radiance Field in the Wild

We introduce im2nerf, a learning framework that predicts a continuous neural object representation given a single input image in the wild, supervised by only segmentation output from off-the-shelf recognition methods.

Our model conditions a neural radiance field on the predicted object representation and uses volume rendering to generate images from novel views.

We conduct extensive quantitative and qualitative experiments on the shapenet dataset as well as qualitative experiments on the open images dataset.

We show that in all cases, im2nerf achieves the state-of-the-art performance for novel view synthesis from a single-viewunposed image in the wild.

Authors

Lu Mi, Abhijit Kundu, David Ross, Frank Dellaert, Noah Snavely, Alireza Fathi

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