Recent advances in 3D data-driven digital twin research have revealed limitations in existing tree reconstruction methods, which rely solely on either scanning or procedural generation. To address this issue, this study proposes a hybrid pipeline that integrates data-driven reconstruction and procedural generation using Gaussian Splatting(GS) data. The proposed method converts multi-view GS outputs into dense point clouds and extracts a stable skeletal structure through color-density-based graph analysis. Fine branches and leaves are procedurally generated using a space colonization algorithm that incorporates botanical principles, achieving a natural and structurally coherent form. Quantitative evaluations using Chamfer distance and Intersection-over-Union metrics demonstrate high geometric similarity and volumetric consistency with the original GS data. The proposed GS-based hybrid framework ensures both visual realism and biological plausibility, enabling efficient and reliable digital twin tree modeling.

ABSTRACT
1. 연구의 필요성 및 목적
2. 연구 방법 및 범위
3. 이론적 배경
    3.1 Gaussian Splatting 및 포인트 클라우드 변환
    3.2 관련연구
    3.3 3D 나무 모델링을 위한 식생학적 원리
4. GS 데이터 기반의 절차적 나무 에셋 생성 파이프라인
    4.1 포인트 클라우드 데이터 정제 (Preprocessing)
    4.2 주요 가지 재구성 (Scaffold Branch Reconstruction)
    4.3 세부 가지 생성 (Lateral Branch Generation)
    4.4 메시화 (Meshing)
    4.5 잎 배치 (Leaf Placement)
5. 연구결과
    5.1 전체 형태 및 구조적 충실도
    5.2 표면 근접도 정밀 분석
6. 결론
Acknowledgement
참고문헌

• Seung-Woo Jeong(Master’s Course, Department of Entertainment Technology, Graduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul 06911, Korea) | 정승우
• Tae-Kyung Yoo(Professor, College of Art & Technology, Chung-Ang University, Anseong 17546, Korea) | 유태경 Corresponding author