标题： ContraGS：用于快速、内存高效重建的代码本浓缩可训练高斯点云 显示英文标题

标题： ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient Reconstruction

摘要： 3D高斯点云（3DGS）是一种最先进的技术，能够以高质量和实时渲染的方式建模现实场景。 通常，通过使用大量3D高斯函数可以实现更高质量的表示。 然而，使用大量的3D高斯函数会显著增加GPU设备内存，用于存储模型参数。 因此，大型模型需要具有高内存容量的强大GPU进行训练，并且由于内存访问和数据移动的低效率，训练/渲染延迟较慢。 在本工作中，我们引入了ContraGS，一种可以在压缩的3DGS表示上直接进行训练的方法，而不会减少高斯函数的数量，从而仅造成少量的模型质量损失。 ContraGS利用代码本在整个训练过程中紧凑地存储一组高斯参数向量，从而显著减少内存消耗。 虽然代码本已被证明在压缩完全训练的3DGS模型方面非常有效，但直接使用代码本表示进行训练是一个未解决的挑战。 ContraGS通过将参数估计问题视为贝叶斯推断问题，解决了代码本压缩表示中学习不可微参数的问题。 为此，ContraGS提供了一个框架，有效地使用MCMC采样来对这些压缩表示的后验分布进行采样。 通过ContraGS，我们证明了ContraGS显著降低了训练期间的峰值内存（平均减少了3.49倍），并加速了训练和渲染（平均分别加快了1.36倍和1.88倍），同时重新训练接近最先进的质量。 显示英文摘要

摘要： 3D Gaussian Splatting (3DGS) is a state-of-art technique to model real-world scenes with high quality and real-time rendering. Typically, a higher quality representation can be achieved by using a large number of 3D Gaussians. However, using large 3D Gaussian counts significantly increases the GPU device memory for storing model parameters. A large model thus requires powerful GPUs with high memory capacities for training and has slower training/rendering latencies due to the inefficiencies of memory access and data movement. In this work, we introduce ContraGS, a method to enable training directly on compressed 3DGS representations without reducing the Gaussian Counts, and thus with a little loss in model quality. ContraGS leverages codebooks to compactly store a set of Gaussian parameter vectors throughout the training process, thereby significantly reducing memory consumption. While codebooks have been demonstrated to be highly effective at compressing fully trained 3DGS models, directly training using codebook representations is an unsolved challenge. ContraGS solves the problem of learning non-differentiable parameters in codebook-compressed representations by posing parameter estimation as a Bayesian inference problem. To this end, ContraGS provides a framework that effectively uses MCMC sampling to sample over a posterior distribution of these compressed representations. With ContraGS, we demonstrate that ContraGS significantly reduces the peak memory during training (on average 3.49X) and accelerated training and rendering (1.36X and 1.88X on average, respectively), while retraining close to state-of-art quality.