Novel view synthesis from sparse views presents a critical and ill-posed challenge in 3D computer vision，primarily due to the severe underconstraint inherent in limited input imagery. Current state-of-the-art approaches can be broadly categorized into two streams： One relies on complex external prior models，such as monocular depth estimators or pretrained diffusion models，to compensate for the information deficit； the other，including methods such as few-shot Gaussian splatting （FSGS），operates within a more self-contained framework. However，a common limitation across these methods is their frequently inadequate reconstruction of detailed regions characterized by high-frequency geometry and thin structures，such as scene edges. Methods dependent on external priors can be misled by their generic nature，while self-contained strategies often lack targeted mechanisms in their densification and regularization processes to model these anisotropic areas effectively. To address these limitations，this study introduces a novel approach for sparse-view synthesis based on 3D Gaussian splatting，which is driven by the concept of edge clusters. The core idea is an “inside-out” optimization paradigm that deliberately avoids heavy external priors，instead seeking to mine and leverage structural information directly from the internal distribution of the 3D Gaussians. The objective is to achieve high-quality novel view synthesis with notably enhanced edge detail fidelity under challenging sparse-input conditions，exploring the potential of a fully self-contained and efficient framework.

The proposed framework is built upon the standard 3D Gaussian splatting pipeline，integrating three key components tailored for sparse view optimization. First，the concept of clustering analysis is introduced into the optimization process. Candidate points for potential edge regions are selected on the basis of their average spatial position gradient norm. These points are subsequently partitioned into clusters using the K-means algorithm. A multicriteria fusion algorithm is then proposed to identify “edge clusters” critical for representing scene structures robustly. This algorithm evaluates four distinct criteria per cluster： cluster-center gradient，average gradient magnitude，cluster spatial scale，and intercluster proximity. A voting mechanism is employed to finalize the edge cluster set，enhancing robustness against the failure of any single criterion. Second，a locally proximity-guided Gaussian unpooling strategy is designed to address under-representation within the identified edge clusters. In contrast to global proximity guidance，this strategy constructs a local proximity graph within each edge cluster. For Gaussians exhibiting insufficient local coverage，new Gaussians are spawned along the connections to their K-nearest neighbors within the cluster，specifically at the midpoints. This approach strategically reinforces the continuity and geometric integrity of edge contours. Third，a collaboratively supervised depth regularization mechanism is introduced to provide stronger geometric constraints，which is particularly beneficial for newly added Gaussians in underconstrained edge regions. This mechanism involves synthesizing pseudoviews for data augmentation and imposing a hybrid depth loss. The hybrid loss combines an absolute depth loss，which uses monocular depth estimates normalized via SfM point clouds to address scale ambiguity，and a relative depth loss based on the Pearson correlation coefficient to preserve depth structure consistency. The depth loss weight follows an exponential decay schedule. The overall loss function integrates standard photometric losses with the proposed absolute and relative depth losses.

Extensive experiments were conducted on the Mip-NeRF360，LLFF，Deep Blending，Tanks&Temples，and DTU datasets under standardized sparse-view settings. The proposed method was quantitatively compared with several state-of-the-art methods，including Mip-NeRF360，RegNeRF，SparseNeRF，vanilla 3DGS，and FSGS. Results demonstrate favorable performance of our approach. On the Mip-NeRF360 dataset at 1/8 resolution，our method achieved average PSNR，SSIM，and LPIPS values of 23.82 dB，0.753，and 0.201，respectively，showing improvements over the FSGS baseline. On the LLFF dataset at 1/8 resolution，it achieved 20.48 dB，0.662，and 0.268，indicating consistent gains. These trends were observed across most tested scenarios and at 1/4 resolution. Qualitatively，visual comparisons reveal that the proposed method successfully recovers fine edge details，such as bicycle spokes and plant stems，with notable clarity and continuity，whereas baseline methods often exhibit blurriness or fragmentation in these areas. The rendering speed remains high，exceeding 275 frames/s，confirming real-time capability. Ablation studies validate the complementary contributions of each proposed module，and further analysis confirms the robustness of the multicriteria clustering to its key parameters.

This study presents a novel edge cluster-driven approach for 3D Gaussian splatting that enhances sparse-view reconstruction quality through an internally structural-aware methodology. By leveraging clustering analysis for targeted edge cluster identification and augmentation，coupled with a collaboratively supervised regularization scheme，the method achieves measurable gains in quantitative metrics and improved visual fidelity for detail-rich regions. It provides a self-contained and efficient pathway for high-quality，real-time synthesis from sparse inputs.