VGOS: Voxel Grid Optimization for View Synthesis from Sparse Inputs

• URL: http://arxiv.org/abs/2304.13386v2
• Date: Fri, 2 Jun 2023 05:39:27 GMT
• Title: VGOS: Voxel Grid Optimization for View Synthesis from Sparse Inputs
• Authors: Jiakai Sun, Zhanjie Zhang, Jiafu Chen, Guangyuan Li, Boyan Ji, Lei Zhao, Wei Xing, Huaizhong Lin
• Abstract summary: VGOS is an approach for fast (3-5 minutes) radiance field reconstruction from sparse inputs (3-10 views) We introduce an incremental voxel training strategy, which prevents overfitting by suppressing the optimization of peripheral voxels. Experiments demonstrate that VGOS achieves state-of-the-art performance for sparse inputs with super-fast convergence.
• Score: 9.374561178958404
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural Radiance Fields (NeRF) has shown great success in novel view synthesis due to its state-of-the-art quality and flexibility. However, NeRF requires dense input views (tens to hundreds) and a long training time (hours to days) for a single scene to generate high-fidelity images. Although using the voxel grids to represent the radiance field can significantly accelerate the optimization process, we observe that for sparse inputs, the voxel grids are more prone to overfitting to the training views and will have holes and floaters, which leads to artifacts. In this paper, we propose VGOS, an approach for fast (3-5 minutes) radiance field reconstruction from sparse inputs (3-10 views) to address these issues. To improve the performance of voxel-based radiance field in sparse input scenarios, we propose two methods: (a) We introduce an incremental voxel training strategy, which prevents overfitting by suppressing the optimization of peripheral voxels in the early stage of reconstruction. (b) We use several regularization techniques to smooth the voxels, which avoids degenerate solutions. Experiments demonstrate that VGOS achieves state-of-the-art performance for sparse inputs with super-fast convergence. Code will be available at https://github.com/SJoJoK/VGOS.

Related papers

• MCGS: Multiview Consistency Enhancement for Sparse-View 3D Gaussian Radiance Fields [73.49548565633123]
  Radiance fields represented by 3D Gaussians excel at synthesizing novel views, offering both high training efficiency and fast rendering. Existing methods often incorporate depth priors from dense estimation networks but overlook the inherent multi-view consistency in input images. We propose a view framework based on 3D Gaussian Splatting, named MCGS, enabling scene reconstruction from sparse input views.
  arXiv  Detail & Related papers  (2024-10-15T08:39:05Z)
• CoherentGS: Sparse Novel View Synthesis with Coherent 3D Gaussians [18.42203035154126]
  We introduce a structured Gaussian representation that can be controlled in 2D image space. We then constraint the Gaussians, in particular their position, and prevent them from moving independently during optimization. We demonstrate significant improvements compared to the state-of-the-art sparse-view NeRF-based approaches on a variety of scenes.
  arXiv  Detail & Related papers  (2024-03-28T15:27:13Z)
• CVT-xRF: Contrastive In-Voxel Transformer for 3D Consistent Radiance Fields from Sparse Inputs [65.80187860906115]
  We propose a novel approach to improve NeRF's performance with sparse inputs. We first adopt a voxel-based ray sampling strategy to ensure that the sampled rays intersect with a certain voxel in 3D space. We then randomly sample additional points within the voxel and apply a Transformer to infer the properties of other points on each ray, which are then incorporated into the volume rendering.
  arXiv  Detail & Related papers  (2024-03-25T15:56:17Z)
• VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis [51.49008959209671]
  We introduce VoxNeRF, a novel approach that leverages volumetric representations to enhance the quality and efficiency of indoor view synthesis. We employ multi-resolution hash grids to adaptively capture spatial features, effectively managing occlusions and the intricate geometry of indoor scenes. We validate our approach against three public indoor datasets and demonstrate that VoxNeRF outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-11-09T11:32:49Z)
• RealLiFe: Real-Time Light Field Reconstruction via Hierarchical Sparse Gradient Descent [23.4659443904092]
  EffLiFe is a novel light field optimization method that produces high-quality light fields from sparse view images in real time. Our method achieves comparable visual quality while being 100x faster on average than state-of-the-art offline methods.
  arXiv  Detail & Related papers  (2023-07-06T14:31:01Z)
• Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction [142.61256012419562]
  We present Voxurf, a voxel-based surface reconstruction approach that is both efficient and accurate. Voxurf addresses the aforementioned issues via several key designs, including 1) a two-stage training procedure that attains a coherent coarse shape and recovers fine details successively, 2) a dual color network that maintains color-geometry dependency, and 3) a hierarchical geometry feature to encourage information propagation across voxels.
  arXiv  Detail & Related papers  (2022-08-26T14:48:02Z)
• Neural Deformable Voxel Grid for Fast Optimization of Dynamic View Synthesis [63.25919018001152]
  We propose a fast deformable radiance field method to handle dynamic scenes. Our method achieves comparable performance to D-NeRF using only 20 minutes for training.
  arXiv  Detail & Related papers  (2022-06-15T17:49:08Z)
• Fast Dynamic Radiance Fields with Time-Aware Neural Voxels [106.69049089979433]
  We propose a radiance field framework by representing scenes with time-aware voxel features, named as TiNeuVox. Our framework accelerates the optimization of dynamic radiance fields while maintaining high rendering quality. Our TiNeuVox completes training with only 8 minutes and 8-MB storage cost while showing similar or even better rendering performance than previous dynamic NeRF methods.
  arXiv  Detail & Related papers  (2022-05-30T17:47:31Z)
• Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction [42.3230709881297]
  We present a super-fast convergence approach to reconstructing the per-scene radiance field from a set of images. Our approach achieves NeRF-comparable quality and converges rapidly from scratch in less than 15 minutes with a single GPU.
  arXiv  Detail & Related papers  (2021-11-22T14:02:07Z)

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.