arXiv:2408.04831v1 Announce Type: new Abstract: Sparse-view 3D reconstruction stands as a formidable challenge in computer vision, aiming to build complete three-dimensional models from a limited array of viewing perspectives. This task confronts several difficulties: 1) the limited number of input images that lack consistent information; 2) dependence on the quality of input images; and 3) the substantial size of model parameters. To address these challenges, we propose a self-augmented coarse-to-fine Gaussian splatting paradigm, enhanced with a structure-aware mask, for sparse-view 3D reconstruction. In particular, our method initially employs a coarse Gaussian model to obtain a basic 3D representation from sparse-view inputs. Subsequently, we develop a fine Gaussian network to enhance consistent and detailed representation of the output with both 3D geometry augmentation and perceptual view augmentation. During training, we design a structure-aware masking strategy to further improve the model’s robustness against sparse inputs and noise.Experimental results on the MipNeRF360 and OmniObject3D datasets demonstrate that the proposed method achieves state-of-the-art performances for sparse input views in both perceptual quality and efficiency.
The article “Sparse-view 3D Reconstruction Using Self-Augmented Coarse-to-Fine Gaussian Splatting Paradigm” addresses the challenges of building complete three-dimensional models from a limited number of viewing perspectives. The limited number of input images, their inconsistent information, and the substantial size of model parameters pose significant difficulties. To overcome these challenges, the authors propose a novel approach that utilizes a self-augmented coarse-to-fine Gaussian splatting paradigm, along with a structure-aware mask. The method initially employs a coarse Gaussian model to obtain a basic 3D representation, which is then enhanced using a fine Gaussian network. This network incorporates 3D geometry augmentation and perceptual view augmentation to improve the consistency and detail of the output. Additionally, a structure-aware masking strategy is designed to enhance the model’s robustness against sparse inputs and noise. Experimental results on the MipNeRF360 and OmniObject3D datasets demonstrate that the proposed method achieves state-of-the-art performance in terms of both perceptual quality and efficiency for sparse input views.
Sparse-View 3D Reconstruction: A New Approach to Overcome Challenges
Sparse-view 3D reconstruction has long been a challenging problem in computer vision. The goal is to build complete three-dimensional models using only a limited number of viewing perspectives. This task presents several difficulties, including a lack of consistent information in the input images, dependence on the quality of those images, and the substantial size of the model parameters. In this article, we propose a novel solution that addresses these challenges and achieves state-of-the-art results in terms of perceptual quality and efficiency.
A Coarse-to-Fine Gaussian Splatting Paradigm
Our approach begins by employing a coarse Gaussian model to obtain a basic 3D representation from the sparse-view inputs. This initial step helps to establish a foundation for further refinement. Next, we introduce a fine Gaussian network that enhances the output representation with both 3D geometry augmentation and perceptual view augmentation. This fine network is designed to capture more detailed and consistent information, overcoming the limitations of sparse inputs.
Structure-Aware Masking Strategy
In order to improve the robustness of our model against sparse inputs and noise, we have developed a structure-aware masking strategy. This strategy helps the network focus on the most informative regions of the input images, disregarding noisy or irrelevant information. By incorporating this structure-aware mask into the training process, we are able to further enhance the performance of our method.
State-of-the-Art Performances
We have evaluated the performance of our proposed method on two benchmark datasets: MipNeRF360 and OmniObject3D. The experimental results demonstrate that our approach achieves state-of-the-art performances in terms of both perceptual quality and efficiency. Our method is able to produce highly detailed and consistent 3D reconstructions from sparse input views, surpassing existing techniques in the field.
Overall, our innovative solution to sparse-view 3D reconstruction offers a new perspective on addressing the challenges in this field. By employing a self-augmented coarse-to-fine Gaussian splatting paradigm and a structure-aware mask, we have achieved remarkable results in terms of perceptual quality and efficiency. This work opens up new possibilities for applications in computer vision, such as virtual reality, robotics, and augmented reality, where accurate 3D reconstructions are essential.
The paper titled “Sparse-View 3D Reconstruction with Self-Augmented Coarse-to-Fine Gaussian Splatting” addresses the challenges faced in building complete three-dimensional models from a limited number of viewing perspectives. Sparse-view 3D reconstruction is a complex task as it relies on a small set of input images that may lack consistent information and are affected by the quality of the images. Moreover, the size of model parameters can be substantial, making the reconstruction process even more challenging.
To overcome these difficulties, the authors propose a novel approach that combines a self-augmented coarse-to-fine Gaussian splatting paradigm with a structure-aware mask. The method starts by using a coarse Gaussian model to obtain a basic 3D representation from the sparse-view inputs. This initial representation serves as a foundation for further refinement. The authors then introduce a fine Gaussian network that enhances the output by incorporating 3D geometry augmentation and perceptual view augmentation. This refinement process aims to achieve a more consistent and detailed representation of the reconstructed 3D model.
During the training phase, the authors incorporate a structure-aware masking strategy to improve the model’s robustness against sparse inputs and noise. This strategy helps the model focus on the relevant information in the input images, reducing the impact of inconsistencies and noise.
The experimental results presented in the paper demonstrate that the proposed method outperforms existing techniques in terms of both perceptual quality and efficiency. The evaluations were conducted on two benchmark datasets, MipNeRF360 and OmniObject3D, showcasing the state-of-the-art performance achieved by the proposed approach.
In conclusion, the paper introduces a promising solution to the challenging task of sparse-view 3D reconstruction. By combining a self-augmented coarse-to-fine Gaussian splatting paradigm with a structure-aware mask, the authors have addressed the limitations of limited input images, image quality, and model parameter size. The experimental results validate the effectiveness of the proposed method, highlighting its potential for advancing the field of computer vision in the context of sparse-view 3D reconstruction.
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