3D Shape Representation
35 papers with code • 0 benchmarks • 4 datasets
These leaderboards are used to track progress in 3D Shape Representation
We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes.
In this work, we introduce DeepSDF, a learned continuous Signed Distance Function (SDF) representation of a class of shapes that enables high quality shape representation, interpolation and completion from partial and noisy 3D input data.
With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance.
Many prior works have focused on _latent-encoded_ neural implicits, where a latent vector encoding of a specific shape is also fed as input.
In this network, a Score Generation Unit is devised to evaluate the quality of each projected image with score vectors.