HR-NeuS: Recovering High-Frequency Surface Geometry via Neural Implicit
Surfaces
- URL: http://arxiv.org/abs/2302.06793v1
- Date: Tue, 14 Feb 2023 02:25:16 GMT
- Title: HR-NeuS: Recovering High-Frequency Surface Geometry via Neural Implicit
Surfaces
- Authors: Erich Liang, Kenan Deng, Xi Zhang, Chun-Kai Wang
- Abstract summary: We present High-Resolution NeuS, a novel neural implicit surface reconstruction method.
HR-NeuS recovers high-frequency surface geometry while maintaining large-scale reconstruction accuracy.
We demonstrate through experiments on DTU and BlendedMVS datasets that our approach produces 3D geometries that are qualitatively more detailed and quantitatively of similar accuracy compared to previous approaches.
- Score: 6.382138631957651
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advances in neural implicit surfaces for multi-view 3D reconstruction
primarily focus on improving large-scale surface reconstruction accuracy, but
often produce over-smoothed geometries that lack fine surface details. To
address this, we present High-Resolution NeuS (HR-NeuS), a novel neural
implicit surface reconstruction method that recovers high-frequency surface
geometry while maintaining large-scale reconstruction accuracy. We achieve this
by utilizing (i) multi-resolution hash grid encoding rather than positional
encoding at high frequencies, which boosts our model's expressiveness of local
geometry details; (ii) a coarse-to-fine algorithmic framework that selectively
applies surface regularization to coarse geometry without smoothing away fine
details; (iii) a coarse-to-fine grid annealing strategy to train the network.
We demonstrate through experiments on DTU and BlendedMVS datasets that our
approach produces 3D geometries that are qualitatively more detailed and
quantitatively of similar accuracy compared to previous approaches.
Related papers
- TetSphere Splatting: Representing High-Quality Geometry with Lagrangian Volumetric Meshes [47.47768820192874]
TetSphere splatting is an explicit, Lagrangian representation for reconstructing 3D shapes with high-quality geometry.
It deforms multiple initial tetrahedral spheres to accurately reconstruct the 3D shape.
It seamlessly integrates into diverse applications, including single-view 3D reconstruction, image-/text-to-3D content generation.
arXiv Detail & Related papers (2024-05-30T17:35:49Z) - Gaussian Opacity Fields: Efficient and Compact Surface Reconstruction in Unbounded Scenes [50.92217884840301]
Gaussian Opacity Fields (GOF) is a novel approach for efficient, high-quality, and compact surface reconstruction in scenes.
GOF is derived from ray-tracing-based volume rendering of 3D Gaussians.
GOF surpasses existing 3DGS-based methods in surface reconstruction and novel view synthesis.
arXiv Detail & Related papers (2024-04-16T17:57:19Z) - SplatFace: Gaussian Splat Face Reconstruction Leveraging an Optimizable Surface [7.052369521411523]
We present SplatFace, a novel Gaussian splatting framework designed for 3D human face reconstruction without reliance on accurate pre-determined geometry.
Our method is designed to simultaneously deliver both high-quality novel view rendering and accurate 3D mesh reconstructions.
arXiv Detail & Related papers (2024-03-27T17:32:04Z) - GeoGaussian: Geometry-aware Gaussian Splatting for Scene Rendering [83.19049705653072]
During the Gaussian Splatting optimization process, the scene's geometry can gradually deteriorate if its structure is not deliberately preserved.
We propose a novel approach called GeoGaussian to mitigate this issue.
Our proposed pipeline achieves state-of-the-art performance in novel view synthesis and geometric reconstruction.
arXiv Detail & Related papers (2024-03-17T20:06:41Z) - Neural Poisson Surface Reconstruction: Resolution-Agnostic Shape
Reconstruction from Point Clouds [53.02191521770926]
We introduce Neural Poisson Surface Reconstruction (nPSR), an architecture for shape reconstruction that addresses the challenge of recovering 3D shapes from points.
nPSR exhibits two main advantages: First, it enables efficient training on low-resolution data while achieving comparable performance at high-resolution evaluation.
Overall, the neural Poisson surface reconstruction not only improves upon the limitations of classical deep neural networks in shape reconstruction but also achieves superior results in terms of reconstruction quality, running time, and resolution agnosticism.
arXiv Detail & Related papers (2023-08-03T13:56:07Z) - Recovering Fine Details for Neural Implicit Surface Reconstruction [3.9702081347126943]
We present D-NeuS, a volume rendering neural implicit surface reconstruction method capable to recover fine geometry details.
We impose multi-view feature consistency on the surface points, derived by interpolating SDF zero-crossings from sampled points along rays.
Our method reconstructs high-accuracy surfaces with details, and outperforms the state of the art.
arXiv Detail & Related papers (2022-11-21T10:06:09Z) - MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface
Reconstruction [72.05649682685197]
State-of-the-art neural implicit methods allow for high-quality reconstructions of simple scenes from many input views.
This is caused primarily by the inherent ambiguity in the RGB reconstruction loss that does not provide enough constraints.
Motivated by recent advances in the area of monocular geometry prediction, we explore the utility these cues provide for improving neural implicit surface reconstruction.
arXiv Detail & Related papers (2022-06-01T17:58:15Z) - Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for
Multi-view Reconstruction [41.43563122590449]
We propose geometry-consistent neural implicit surfaces learning for multi-view reconstruction.
Our proposed method achieves high-quality surface reconstruction in both complex thin structures and large smooth regions.
arXiv Detail & Related papers (2022-05-31T14:52:07Z) - Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid
Representations [21.64457003420851]
We develop a hybrid neural surface representation that allows us to impose geometry-aware sampling and regularization.
We demonstrate that our method can be adopted to improve techniques for reconstructing neural implicit surfaces from multi-view images or point clouds.
arXiv Detail & Related papers (2020-12-11T15:51:04Z) - Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images [64.53227129573293]
We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views.
We design neural networks capable of generating high-quality parametric 3D surfaces which are consistent between views.
Our method is supervised and trained on a public dataset of shapes from common object categories.
arXiv Detail & Related papers (2020-08-18T06:33:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.