paper-with-me

홈 › Papers

Duplex-GS: Proxy-Guided Weighted Blending for Real-Time Order-Independent Gaussian Splatting

2025-08-05 · Weihang Liu, Yuke Li, Yuxuan Li, Jingyi Yu, Xin Lou arxiv

Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated remarkable rendering fidelity and efficiency. However, these methods still rely on computationally expensive sequential alpha-blending operations, resulting in significant overhead, particularly on resource-constrained platforms. In this paper, we propose Duplex-GS, a dual-hierarchy framework that integrates proxy Gaussian representations with order-independent rendering techniques to achieve photorealistic results while sustaining real-time performance. To mitigate the overhead caused by view-adaptive radix sort, we introduce cell proxies for local Gaussians management and propose cell search rasterization for further acceleration. By seamlessly combining our framework with Order-Independent Transparency (OIT), we develop a physically inspired weighted sum rendering technique that simultaneously eliminates "popping" and "transparency" artifacts, yielding substantial improvements in both accuracy and efficiency. Extensive experiments on a variety of real-world datasets demonstrate the robustness of our method across diverse scenarios, including multi-scale training views and large-scale environments. Our results validate the advantages of the OIT rendering paradigm in Gaussian Splatting, achieving high-quality rendering with an impressive 1.5 to 4 speedup over existing OIT based Gaussian Splatting approaches and 52.2% to 86.9% reduction of the radix sort overhead without quality degradation.

📄 PDF Abstract BibTeX arXiv:2508.03180

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending

2025-08-28 · Anirudh Satheesh, Keenan Powell, Hua Wei arxiv

Many multi-agent reinforcement learning (MARL) algorithms are trained in fixed simulation environments, making them brittle when deployed in real-world scenarios with more complex and uncertain conditions. Contextual MAR…

Multi-agent Reinforcement Learning

DiffTex: Differentiable Texturing for Architectural Proxy Models

2025-09-27 · Weidan Xiong, Yongli Wu, Bochuan Zeng, Jianwei Guo 외 arxiv

Simplified proxy models are commonly used to represent architectural structures, reducing storage requirements and enabling real-time rendering. However, the geometric simplifications inherent in proxies result in a loss…

Data Quality Profiling at Scale with Progressive Sampling: A Benchmark for Data-Centric AI Pipelines

2026-07-28 · Laure Berti-Equille arxiv

Data quality profiling -- computing missing-value rates, duplicate fractions, outlier densities, and functional-dependency violations -- is foundational for data-centric AI pipelines, yet exhaustive scans over millions o…

BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation

2021-10-22 · NeurIPS 2021 12 · Mingcong Liu, Qiang Li, Zekui Qin, Guoxin Zhang 외

Generative Adversarial Networks (GANs) have made a dramatic leap in high-fidelity image synthesis and stylized face generation. Recently, a layer-swapping mechanism has been developed to improve the stylization performan…

DiversityFace GenerationImage Generation

Real-World Evaluation of Full-Duplex Millimeter Wave Communication Systems

2023-07-20 · Ian P. Roberts, Yu Zhang, Tawfik Osman, Ahmed Alkhateeb

Noteworthy strides continue to be made in the development of full-duplex millimeter wave (mmWave) communication systems, but most of this progress has been built on theoretical models and validated through simulation. In…