Recent advances in sparse-view 3D reconstruction have focused on new view synthesis and scene representation techniques. Methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have demonstrated significant success in accurately reconstructing complex real-world scenes. Researchers have proposed several enhancements to improve performance, speed, and quality. Sparse-view scene reconstruction techniques employ regularization methods and generalizable reconstruction priors to address the challenges of limited input views. Recent approaches such as SparseGS, pixelSplat, and MVSplat have further improved these foundations.
Unposed scene reconstruction remains challenging as many existing methods rely on known camera poses. Techniques such as iNeRF, NeRFmm, BARF, and GARF have explored strategies to estimate and optimize camera poses along with scene representation. However, these methods still face difficulties with complex camera trajectories. The introduction of LM-Gaussian represents a new direction in this field, incorporating large prior models to improve reconstruction quality from limited images. This approach builds on previous work while addressing persistent challenges in sparse-view 3D reconstruction.
LM-Gaussian addresses sparse-view 3D reconstruction challenges by generating high-quality results from limited input images. The method incorporates a robust initialization module that uses stereo priors for camera pose recovery and reliable point cloud generation. An iterative Gaussian refinement module employs diffusion-based techniques to enhance image details and preserve scene features during 3D Gaussian spatter optimization. Video diffusion priors further enhance rendered images to achieve realistic visual effects. This approach significantly reduces data acquisition requirements while maintaining high-quality 360-degree scene reconstruction. Experiments on public datasets validate the effectiveness of the framework in practical applications.
Previous 3D reconstruction methods such as 3D Gaussian splatting require numerous input images, making them impractical for real-world applications. These approaches struggle with sparse view scenarios, leading to initialization failures, overfitting, and loss of details. Existing solutions employing frequency and depth regularization still produce messy results due to the reliance on traditional structure-from-motion methods. LM-Gaussian addresses these limitations by integrating multiple large a priori models. The method consists of four key modules: background-aware depth-guided initialization, multi-modal regularized Gaussian reconstruction, iterative Gaussian refinement module, and a video diffusion prior.
The LM-Gaussian initialization module uses DUSt3R stereo priors for camera pose estimation and point cloud creation. The reconstruction process employs photometric loss and additional constraints to optimize 3D models. The iterative refinement module applies a diffusion-based Gaussian repair model to improve image quality and incorporate high-frequency details. Validation experiments on public datasets demonstrate LM-Gaussian’s ability to produce high-quality 360-degree scene reconstructions with significantly reduced data acquisition requirements. This comprehensive methodology effectively addresses the challenges of sparse-view 3D reconstruction through innovative initialization, regularization, and refinement techniques.
LM-Gaussian demonstrates significant advances in 3D reconstruction of sparse views, outperforming baseline methods such as DNGaussian and SparseNerf. Quantitative metrics including PSNR, SSIM, and LPIPS show improved reconstruction quality and finer details in rendered images. The method excels with limited input data, achieving high-quality reconstructions from only 16 images. Multimodal regularization techniques improve performance, resulting in smoother surfaces and reduced artifacts. LM-Gaussian consistently outperforms the original 3DGS across varying amounts of input images, although its advantages diminish in denser settings.
The effectiveness of the method is particularly evident in sparse vision scenarios, where it preserves structures and details better than its competitors. Improvements in visual quality include smoother surfaces and fewer artifacts such as black holes and sharp angles. LM-Gaussian significantly reduces data acquisition requirements compared to traditional 3DGS methods, while maintaining high-quality results in 360-degree scenes. These achievements position LM-Gaussian as a robust solution for practical 3D reconstruction applications, effectively addressing the challenges of limited input data and demonstrating superior performance under sparse vision conditions.
In conclusion, LM-Gaussian presents a new approach to 3D reconstruction of sparse views, leveraging priors from large vision models. The method incorporates a robust initialization module, multimodal regularizations, and iterative diffusion refinement to improve reconstruction quality and avoid overfitting. It significantly reduces data acquisition requirements while achieving high-quality results in complex 360-degree scenes. Although currently limited to static scenes, LM-Gaussian demonstrates substantial advances in the field. Future work aims to incorporate dynamic 3DGS methods, which could expand the applicability of the method to dynamic modeling and further improve its effectiveness in various 3D reconstruction scenarios.
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Shoaib Nazir is a Consulting Intern at MarktechPost and has completed his dual M.tech degree from Indian Institute of technology (IIT) Kharagpur. Being passionate about data science, he is particularly interested in the various applications of artificial intelligence in various domains. Shoaib is driven by the desire to explore the latest technological advancements and their practical implications in everyday life. His enthusiasm for innovation and solving real-world problems fuels his continuous learning and contribution to the field of ai.
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