LandMarkSystem Technical Report

2025-03-27

AI Summary

Traditional deep learning frameworks face bottlenecks in 3D sparse computation efficiency, model compatibility, and resource scalability for large-scale 3D reconstruction. To address these challenges, this paper proposes LandMarkSystem—a lightweight, modular computational framework. Its core contributions are: (1) a novel componentized adaptation layer supporting heterogeneous 3D representations—including Neural Radiance Fields (NeRF) and 3D Gaussian Splatting; (2) a dynamic, resource-aware model loading and parameter offloading mechanism; and (3) a dedicated operator library optimized for 3D sparse computation. Experiments demonstrate substantial improvements in training throughput and real-time rendering speed for large-scale scenes. Comprehensive evaluations across multiple benchmarks confirm cross-model compatibility and robust performance under constrained hardware resources—e.g., limited GPU memory and compute capacity—validating the framework’s practicality and scalability.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

3D reconstruction is vital for applications in autonomous driving, virtual reality, augmented reality, and the metaverse. Recent advancements such as Neural Radiance Fields(NeRF) and 3D Gaussian Splatting (3DGS) have transformed the field, yet traditional deep learning frameworks struggle to meet the increasing demands for scene quality and scale. This paper introduces LandMarkSystem, a novel computing framework designed to enhance multi-scale scene reconstruction and rendering. By leveraging a componentized model adaptation layer, LandMarkSystem supports various NeRF and 3DGS structures while optimizing computational efficiency through distributed parallel computing and model parameter offloading. Our system addresses the limitations of existing frameworks, providing dedicated operators for complex 3D sparse computations, thus facilitating efficient training and rapid inference over extensive scenes. Key contributions include a modular architecture, a dynamic loading strategy for limited resources, and proven capabilities across multiple representative algorithms.This comprehensive solution aims to advance the efficiency and effectiveness of 3D reconstruction tasks.To facilitate further research and collaboration, the source code and documentation for the LandMarkSystem project are publicly available in an open-source repository, accessing the repository at: https://github.com/InternLandMark/LandMarkSystem.

Problem

• Enhances multi-scale 3D scene reconstruction and rendering efficiency
• Addresses limitations in existing deep learning frameworks for large-scale scenes
• Optimizes computational performance via distributed parallel computing and parameter offloading

Innovation

• Componentized model adaptation layer for NeRF and 3DGS
• Distributed parallel computing for efficiency optimization
• Dynamic loading strategy for limited resource management