FreeSplat++：面向高效室内场景重建的可泛化3D高斯泼溅
FreeSplat++: Generalizable 3D Gaussian Splatting for Efficient Indoor Scene Reconstruction
AI总结：
提出FreeSplat++，通过低成本跨视图聚合、像素级三元组融合和加权漂浮物去除，实现高效的大规模室内全场景3D高斯重建，并利用深度正则化微调提升精度与速度。
摘要：
近年来，将高效的前馈方案集成到3D高斯泼溅（3DGS）中已被积极探索。然而，大多数现有方法侧重于小区域的稀疏视图重建，无法在质量或效率方面产生合格的全场景重建结果。在本文中，我们提出FreeSplat++，其重点在于扩展可泛化的3DGS，使其成为大规模室内全场景重建的替代方法，这有可能显著加速重建速度并提高几何精度。为促进全场景重建，我们首先提出低成本跨视图聚合框架，以高效处理极长的输入序列。随后，我们引入一种精心设计的像素级三元组融合方法，以从多个视图增量聚合重叠的3D高斯基元，自适应地减少其冗余。此外，我们提出一种加权漂浮物去除策略，可有效减少漂浮物，该策略作为一种显式深度融合方法，在全场景重建中至关重要。在前馈重建3DGS基元之后，我们研究了深度正则化的逐场景微调过程。利用在前馈预测阶段获得的密集、多视图一致的深度图作为额外约束，我们细化整个场景的3DGS基元，以提高渲染质量，同时保持几何精度。大量实验证实，我们的FreeSplat++显著优于现有的可泛化3DGS方法，尤其是在全场景重建中。与传统的逐场景优化3DGS方法相比，我们的带有深度正则化逐场景微调的方法在重建精度上表现出显著提升，并大幅减少了训练时间。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
