SPARS3R: Semantic Prior Alignment and Regularization for Sparse 3D Reconstruction

Abstract: Recent efforts in Gaussian-Splat-based Novel View Synthesis can achieve photorealistic rendering; however, such capability is limited in sparse-view scenarios due to sparse initialization and over-fitting floaters. Recent progress in depth estimation and alignment can provide dense point cloud with few views; however, the resulting pose accuracy is suboptimal. In this work, we present SPARS3R, which combines the advantages of accurate pose estimation from Structure-from-Motion and dense point cloud from depth estimation. To this end, SPARS3R first performs a Global Fusion Alignment process that maps a prior dense point cloud to a sparse point cloud from Structure-from-Motion based on triangulated correspondences. RANSAC is applied during this process to distinguish inliers and outliers. SPARS3R then performs a second, Semantic Outlier Alignment step, which extracts semantically coherent regions around the outliers and performs local alignment in these regions. Along with several improvements in the evaluation process, we demonstrate that SPARS3R can achieve photorealistic rendering with sparse images and significantly outperforms existing approaches.

• The paper introduces a novel SPARS3R framework that integrates semantic prior alignment with dense-sparse fusion to improve pose estimation and 3D reconstruction.
• It employs a two-stage process combining global fusion alignment using Procrustes and RANSAC with localized semantic outlier alignment for refined depth accuracy.
• Experiments on benchmark datasets demonstrate significant gains in reconstruction quality and photorealistic rendering, benefiting fields like autonomous driving and robotics.

Semantic Prior Alignment and Regularization for Sparse 3D Reconstruction

The paper, "SPARS3R: Semantic Prior Alignment and Regularization for Sparse 3D Reconstruction," presents a novel approach to enhancing sparse-view 3D scene reconstruction and novel view synthesis (NVS) by addressing the limitations posed by sparse point clouds and suboptimal pose estimation. This work introduces the SPARS3R framework, which leverages semantic prior alignment and regularization techniques to optimize the initialization and pose accuracy in 3D Gaussian Splatting (3DGS) frameworks.

Core Contribution and Methodology

SPARS3R primarily aims to improve reconstruction quality in sparse-view scenarios by integrating dense point clouds obtained from depth estimation with precise camera poses derived from Structure-from-Motion (SfM). The central approach is divided into two pivotal stages:

1. Global Fusion Alignment: This initial step involves the fusion of dense point clouds from depth estimation models, like DUSt3R and MASt3R, with a sparse yet geometrically accurate point cloud from SfM. This fusion is achieved through a Procrustes Alignment process, enhanced with RANSAC to filter outliers, ensuring the global optimization of the transformation process.
2. Semantic Outlier Alignment: Recognizing inherent inaccuracies due to depth discrepancies, the second stage employs semantic segmentation to isolate and locally align problematic regions around outliers identified in the initial alignment. By grouping semantically coherent 2D regions and applying localized transformations, this step significantly refines the alignment, yielding a dense point cloud that accurately matches the underlying geometric and pose-based constraints.

Experimental Evaluation

The efficacy of the SPARS3R method was rigorously tested against baseline methods on benchmark datasets, including Tanks and Temples, MVimgNet, and Mip-NeRF 360. The integration of semantic segmentation and advanced alignment processes in SPARS3R led to consistently superior performance compared to state-of-the-art techniques, as evidenced by robust quantitative improvements in metrics such as PSNR and DSIM. Notably, SPARS3R outperformed existing methods in scenarios with sparse view input, demonstrating its capability to produce photorealistic rendering with minimal input data.

Implications and Future Research

The implications of SPARS3R are significant in fields that rely on accurate 3D scene reconstruction from limited imagery, including autonomous driving, robotics, and urban planning. By effectively merging dense point clouds with precise camera calibration, SPARS3R not only enhances rendering quality but also reduces reliance on dense input datasets, marking a critical advancement for applications where data acquisition is restricted.

Future investigations could explore the deployment of non-rigid transformations to further optimize local alignment processes. Additionally, integrating advanced interactive segmentation models could refine the semantic outlier alignment, offering better adaptability to varied scene complexities. SPARS3R paves the way for more efficient and accurate 3D reconstruction methodologies, highlighting the potential of integrating semantic insights with photorealistic rendering techniques.