We propose Long-LRM, a generalizable 3D Gaussian reconstruction model that is capable of reconstructing a large scene from a long sequence of input images. Specifically, our model can process 32 source images at 960x540 resolution within only 1.3 seconds on a single A100 80G GPU. Our architecture features a mixture of the recent Mamba2 blocks and the classical transformer blocks which allowed many more tokens to be processed than prior work, enhanced by efficient token merging and Gaussian pruning steps that balance between quality and efficiency. Unlike previous feed-forward models that are limited to processing 1~4 input images and can only reconstruct a small portion of a large scene, Long-LRM reconstructs the entire scene in a single feed-forward step. On large-scale scene datasets such as DL3DV-140 and Tanks and Temples, our method achieves performance comparable to optimization-based approaches while being two orders of magnitude more efficient. Project page: https://arthurhero.github.io/projects/llrm

本研究解决了传统模型在大场景重建中只能处理少量输入图像的瓶颈问题。我们提出的Long-LRM模型，结合现代Mamba2块与经典变换块，能够在1.3秒内处理32张960x540分辨率的图像，显著提升了处理效率与质量。实验表明，该方法在DL3DV-140和Tanks and Temples等大规模场景数据集上的表现与基于优化的方法相当，但效率提高了两个数量级。

长序列大重建模型用于广覆盖高斯点云