FreeSplat++: Generalizable 3D Gaussian Splatting for Efficient Indoor Scene Reconstruction (2503.22986v1)

Abstract: Recently, the integration of the efficient feed-forward scheme into 3D Gaussian Splatting (3DGS) has been actively explored. However, most existing methods focus on sparse view reconstruction of small regions and cannot produce eligible whole-scene reconstruction results in terms of either quality or efficiency. In this paper, we propose FreeSplat++, which focuses on extending the generalizable 3DGS to become an alternative approach to large-scale indoor whole-scene reconstruction, which has the potential of significantly accelerating the reconstruction speed and improving the geometric accuracy. To facilitate whole-scene reconstruction, we initially propose the Low-cost Cross-View Aggregation framework to efficiently process extremely long input sequences. Subsequently, we introduce a carefully designed pixel-wise triplet fusion method to incrementally aggregate the overlapping 3D Gaussian primitives from multiple views, adaptively reducing their redundancy. Furthermore, we propose a weighted floater removal strategy that can effectively reduce floaters, which serves as an explicit depth fusion approach that is crucial in whole-scene reconstruction. After the feed-forward reconstruction of 3DGS primitives, we investigate a depth-regularized per-scene fine-tuning process. Leveraging the dense, multi-view consistent depth maps obtained during the feed-forward prediction phase for an extra constraint, we refine the entire scene's 3DGS primitive to enhance rendering quality while preserving geometric accuracy. Extensive experiments confirm that our FreeSplat++ significantly outperforms existing generalizable 3DGS methods, especially in whole-scene reconstructions. Compared to conventional per-scene optimized 3DGS approaches, our method with depth-regularized per-scene fine-tuning demonstrates substantial improvements in reconstruction accuracy and a notable reduction in training time.

Overview of FreeSplat++ for Indoor Scene Reconstruction

FreeSplat++ introduces an advanced framework focused on enhancing 3D Gaussian Splatting (3DGS) for reconstructing large-scale indoor scenes efficiently and with geometric accuracy. The paper proposes a transformative approach which extends generalizable 3DGS to perform whole-scene reconstructions, thereby addressing conventional limitations related to sparse-view optimization and inefficiency in handling extensive scenes.

Key Contributions

The initial contribution of FreeSplat++ is the Low-cost Cross-View Aggregation framework that efficiently manages long input sequences for entire scene reconstruction without substantial computational overhead. This framework incorporates CNN-based backbone networks to process dense sequences of images, which is notable for optimizing resource usage compared to existing methods that often impose heavier overhead using transformer-based architectures.

Furthermore, the paper introduces a Pixel-wise Triplet Fusion (PTF) methodology that incrementally aggregates overlapping 3D Gaussian primitives across multiple views. This fusion process cleverly eliminates redundant primitives by aligning local and global Gaussian triplets based on pixel-wise correspondence, marking an advancement from previous methods which lacked adaptive fusion mechanisms.

Additionally, FreeSplat++ proposes a Weighted Floater Removal strategy, leveraging accumulated weights from the PTF process. It performs depth-consistent checks across multiple views, effectively mitigating issues caused by floaters that can degrade rendering quality and accuracy. This strategy is comparable in purpose to traditional TSDF Fusion due to its focus on maintaining consistency, although it accomplishes this with greater efficiency and integration into the generalizable framework.

Finally, the framework facilitates a depth-regularized per-scene fine-tuning step, refining 3DGS primitives with multi-view depth regularization, which further enhances rendering quality while preserving geometric accuracy. This fine-tuning process significantly reduces training time while achieving improved extrapolation and interpolation rendering results compared to traditional per-scene optimized methods.

Analysis and Results

Extensive experiments demonstrated that FreeSplat++ notably outperforms existing generalizable 3DGS approaches in achieving higher geometric accuracy within reduced training times. Compared to traditional methods, FreeSplat++ showed marked improvements in rendering quality, efficiency, and ability to handle complex indoor environments. It reduced average training time significantly while maintaining competitive depth accuracy and rendering quality, which is crucial for practical largescale applications.

Additionally, FreeSplat++ excels in depth-accurate scene reconstruction in extrapolated views, leveraging unsupervised techniques to achieve rendering results consistent with the ground truth depths. The fine-tuning results exhibit especially improved performance over baseline methods, showcasing how depth regularization effectively enhances overall rendering consistency and quality.

Practical and Theoretical Implications

The implications for FreeSplat++ are profound in both theoretical and practical domains. Theoretically, the framework demonstrates the efficacy of combining CNN backbone with innovative fusion and removal strategies to eliminate superfluous Gaussian primitives and mitigate depth inconsistencies. Practically, FreeSplat++ opens pathways for real-time large-scale scene reconstructions in various fields such as virtual reality and architectural visualization, where quick and accurate scene rendering is indispensable.

Future Developments

This research paves the path for further exploration into refining fusion mechanisms and integrating better consistency constraints during fine-tuning, to eventually replace per-scene optimization entirely. Future studies could delve into extending FreeSplat++’s applicability to outdoor environments and broader datasets to maximize its utility across diverse scene types. Additionally, incorporating adaptive fusion strategies would enhance seamless integration for dynamic environments.

Overall, FreeSplat++ emerges as a pivotal development in the generalizable 3D scene reconstruction field, bridging the gap between theoretical advancements and practical applications, potentially revolutionizing how large-scale scenes are rendered in real-time.