Neuralangelo: High-Fidelity Neural Surface Reconstruction [PDF]
Computer Vision and Pattern Recognition, 2023 Neural surface reconstruction has been shown to be powerful for recovering dense 3D surfaces via image-based neural rendering. However, current methods struggle to recover detailed structures of real-world scenes.
Zhaoshuo Li +6 more
semanticscholar +1 more source
RealFusion 360° Reconstruction of Any Object from a Single Image [PDF]
Computer Vision and Pattern Recognition, 2023 We consider the problem of reconstructing a full 360° photographic model of an object from a single image of it. We do so by fitting a neural radiance field to the image, but find this problem to be severely ill-posed.
Luke Melas-Kyriazi +3 more
semanticscholar +1 more source
Occupancy Networks: Learning 3D Reconstruction in Function Space [PDF]
Computer Vision and Pattern Recognition, 2018 With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity. However, unlike for images, in 3D there is no canonical representation which is both computationally and memory efficient yet allows for ...
L. Mescheder +4 more
semanticscholar +1 more source
MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction [PDF]
Neural Information Processing Systems, 2022 In recent years, neural implicit surface reconstruction methods have become popular for multi-view 3D reconstruction. In contrast to traditional multi-view stereo methods, these approaches tend to produce smoother and more complete reconstructions due to
Zehao Yu +4 more
semanticscholar +1 more source
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information [PDF]
IEEE Transactions on Information Theory, 2004 This paper considers the model problem of reconstructing an object from incomplete frequency samples. Consider a discrete-time signal f/spl isin/C/sup N/ and a randomly chosen set of frequencies /spl Omega/.
E. Candès, J. Romberg, T. Tao
semanticscholar +1 more source
Splatter Image: Ultra-Fast Single-View 3D Reconstruction [PDF]
Computer Vision and Pattern Recognition, 2023 We introduce the Splatter Image, an ultra-efficient approach for monocular 3D object reconstruction. Splatter Image is based on Gaussian Splatting, which allows fast and high-quality reconstruction of 3D scenes from multiple images.
Stanislaw Szymanowicz +2 more
semanticscholar +1 more source
NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction [PDF]
IEEE International Conference on Computer Vision, 2022 Recent methods for neural surface representation and rendering, for example NeuS [59], have demonstrated the remarkably high-quality reconstruction of static scenes.
Yiming Wang +5 more
semanticscholar +1 more source
MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo [PDF]
IEEE International Conference on Computer Vision, 2021 We present MVSNeRF, a novel neural rendering approach that can efficiently reconstruct neural radiance fields for view synthesis. Unlike prior works on neural radiance fields that consider per-scene optimization on densely captured images, we propose a ...
Anpei Chen +6 more
semanticscholar +1 more source
UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction [PDF]
IEEE International Conference on Computer Vision, 2021 Neural implicit 3D representations have emerged as a powerful paradigm for reconstructing surfaces from multi-view images and synthesizing novel views.
Michael Oechsle +2 more
semanticscholar +1 more source
Reconstruction of Integers from Pairwise Distances [PDF]
, 2012 Given a set of integers, one can easily construct the set of their pairwise distances. We consider the inverse problem: given a set of pairwise distances, find the integer set which realizes the pairwise distance set.
Hassibi, Babak, Jaganathan, Kishore
core +2 more sources