Add to ORKGNeural 3D implicit representations learn priors that are useful for diverse applications, such as single- or multiple-view 3D reconstruction. A major downside of existing approaches while rendering an image is that they require evaluating the network multiple times per camera ray so that the high computational time forms a bottleneck for downstream applications. We address this problem by introducing a novel neural scene representation that we call the directional distance function (DDF). To this end, we learn a signed distance function (SDF) along with our DDF model to represent a class of shapes. Specifically, our DDF is defined on the unit sphere and predicts the distance to the surface along any given direction. Therefore, our DDF allow...
In recent years, implicit functions have drawn attention in the field of 3D reconstruction and have ...
We are witnessing an explosion of neural implicit representations in computer vision and graphics. T...
Neural implicit representations, which encode a surface as the level set of a neural network applied...
Implicit neural representations of 3D shapes form strong priors that areuseful for various applicati...
We introduce a novel approach for rendering static and dynamic 3D neural signed distance functions (...
We present a novel method, called NeuralUDF, for reconstructing surfaces with arbitrary topologies f...
Scene Completion is the task of completing missing geometry from a partial scan of a scene. Most pre...
Probabilistic diffusion models have achieved state-of-the-art results for image synthesis, inpaintin...
Recently, neural implicit surfaces learning by volume rendering has become popular for multi-view re...
NeRF-based techniques fit wide and deep multi-layer perceptrons (MLPs) to a continuous radiance fiel...
Scene representation is the process of converting sensory observations of an environment into compac...
In this work, we present a new method for 3D face reconstruction from sparse-view RGB images. Unlike...
Multi-View Stereo (MVS) is a core task in 3D computer vision. With the surge of novel deep learning ...
Learning neural implicit representations has achieved remarkable performance in 3D reconstruction fr...
In this paper, we address the problem of multi-view 3D shape reconstruction. While recent differenti...
In recent years, implicit functions have drawn attention in the field of 3D reconstruction and have ...
We are witnessing an explosion of neural implicit representations in computer vision and graphics. T...
Neural implicit representations, which encode a surface as the level set of a neural network applied...
Implicit neural representations of 3D shapes form strong priors that areuseful for various applicati...
We introduce a novel approach for rendering static and dynamic 3D neural signed distance functions (...
We present a novel method, called NeuralUDF, for reconstructing surfaces with arbitrary topologies f...
Scene Completion is the task of completing missing geometry from a partial scan of a scene. Most pre...
Probabilistic diffusion models have achieved state-of-the-art results for image synthesis, inpaintin...
Recently, neural implicit surfaces learning by volume rendering has become popular for multi-view re...
NeRF-based techniques fit wide and deep multi-layer perceptrons (MLPs) to a continuous radiance fiel...
Scene representation is the process of converting sensory observations of an environment into compac...
In this work, we present a new method for 3D face reconstruction from sparse-view RGB images. Unlike...
Multi-View Stereo (MVS) is a core task in 3D computer vision. With the surge of novel deep learning ...
Learning neural implicit representations has achieved remarkable performance in 3D reconstruction fr...
In this paper, we address the problem of multi-view 3D shape reconstruction. While recent differenti...
In recent years, implicit functions have drawn attention in the field of 3D reconstruction and have ...
We are witnessing an explosion of neural implicit representations in computer vision and graphics. T...
Neural implicit representations, which encode a surface as the level set of a neural network applied...