DGTR: Distributed Gaussian Turbo-Reconstruction for Sparse-View Vast Scenes (2411.12309v2)

Abstract: Novel-view synthesis (NVS) approaches play a critical role in vast scene reconstruction. However, these methods rely heavily on dense image inputs and prolonged training times, making them unsuitable where computational resources are limited. Additionally, few-shot methods often struggle with poor reconstruction quality in vast environments. This paper presents DGTR, a novel distributed framework for efficient Gaussian reconstruction for sparse-view vast scenes. Our approach divides the scene into regions, processed independently by drones with sparse image inputs. Using a feed-forward Gaussian model, we predict high-quality Gaussian primitives, followed by a global alignment algorithm to ensure geometric consistency. Synthetic views and depth priors are incorporated to further enhance training, while a distillation-based model aggregation mechanism enables efficient reconstruction. Our method achieves high-quality large-scale scene reconstruction and novel-view synthesis in significantly reduced training times, outperforming existing approaches in both speed and scalability. We demonstrate the effectiveness of our framework on vast aerial scenes, achieving high-quality results within minutes. Code will released on our [https://3d-aigc.github.io/DGTR].

• The paper introduces a distributed framework that leverages a feed-forward Gaussian model to predict scene partitions from sparse-view imagery.
• It employs decentralized drone processing and a global alignment algorithm to ensure accurate geometric consistency across reconstructions.
• Results show significant reductions in training time and improved reconstruction quality, enabling real-time applications in autonomous systems.

An Expert Evaluation of DGTR: Distributed Gaussian Turbo-Reconstruction for Sparse-View Vast Scenes

The paper "DGTR: Distributed Gaussian Turbo-Reconstruction for Sparse-View Vast Scenes" presents an innovative approach for addressing the limitations in novel-view synthesis (NVS) within the field of vast scene reconstruction. Traditional methods, heavily reliant on dense image inputs and extensive resources, face challenges in environments with constrained computational capabilities. This research introduces DGTR, a distributed framework aimed at enhancing the efficacy of reconstructing large-scale scenes using sparse-view imagery, notably employing drones for decentralized processing.

Methodological Innovations

DGTR stands distinguished by its deployment of a feed-forward Gaussian model to facilitate high-quality Gaussian primitive prediction. This model predicts scene divisions into regions processed distinctly by several drones, each operating with minimal image inputs. A notable aspect is the inclusion of a global alignment algorithm designed to ensure geometric consistency across fragmented reconstructions, thereby minimizing errors prevalent in conventional methods.

The approach is strengthened by the incorporation of synthetic views and depth priors, enhancing training by providing auxiliary supervision to the reconstruction process. The presented framework introduces a distillation-based model aggregation mechanism, effectively compiling regional reconstructions into a coherent whole, thereby optimizing efficiency without compromising on quality.

Performance and Implications

The empirical results indicate a significant reduction in training times while outperforming existing methodologies in terms of scalability and reconstruction quality. DGTR's ability to achieve rapid scene reconstruction within a matter of minutes sets a new precedent in the domain, opening avenues for real-time applications in autonomous systems and aerial surveying.

From a theoretical standpoint, DGTR illustrates the potential of distributed systems in overcoming conventional limitations of global initialization and dense input reliance. It establishes a framework where independent system components contribute asynchronously towards a collective goal, aligning with advancements in federated architectures.

Future Directions

The implications of this research extend towards enhancing real-time applications of NVS, particularly in autonomous navigation and real-world scene synthesis. Future work can explore the scalability of DGTR in even broader environments, potentially integrating advanced machine learning models to further streamline the Gaussian initialization and alignment process.

Significantly, this approach paves the way for exploring distributed computing models in other aspects of computer vision and robotics, potentially influencing how large-scale data is handled in resource-constrained environments. The open-source release of DGTR on the project page will likely spur further research and adaptation of the methodology across various applications.

In summary, DGTR exemplifies a novel convergence of distributed processing and efficient reconstruction tailored for vast scenes, offering practical solutions alongside theoretical advancements in scalable scene synthesis.