Chorus：多教师预训练用于整体3D高斯场景编码
Chorus: Multi-Teacher Pretraining for Holistic 3D Gaussian Scene Encoding
AI总结：
Chorus通过多教师预训练方法，实现对3D高斯场景的高效编码，提升多任务表现并实现数据高效监督。
摘要：
尽管3DGS已成为高保真的场景表示，但直接从其基本元素中编码丰富且通用的特征仍然研究不足。我们通过引入Chorus，一种多教师预训练框架，来解决这一差距。Chorus通过蒸馏来自2D基础模型的互补信号，学习一个整体的前馈3D高斯散射（3DGS）场景编码器。Chorus采用共享的3D编码器和教师特定的投影器，从语言对齐、通用和物体感知的教师中学习，鼓励一个共享的嵌入空间，该空间能够捕捉从高层语义到细粒度结构的信号。我们评估了Chorus在多种任务上的表现：开放词汇语义和实例分割、线性和解码器探测，以及数据高效监督。除了3DGS外，我们还测试了Chorus在几个仅支持点云的基准上，通过预训练一个仅使用高斯中心、颜色和估计法线作为输入的变体。有趣的是，这种编码器表现出强大的迁移能力，在使用9.9次更少的训练场景时，优于点云基线。最后，我们提出了渲染和蒸馏适应，以促进领域外微调。我们的代码和模型将在发表时发布。
英文摘要：
While 3DGS has emerged as a high-fidelity scene representation, encoding rich, general-purpose features directly from its primitives remains under-explored. We address this gap by introducing Chorus, a multi-teacher pretraining framework that learns a holistic feed-forward 3D Gaussian Splatting (3DGS) scene encoder by distilling complementary signals from 2D foundation models. Chorus employs a shared 3D encoder and teacher-specific projectors to learn from language-aligned, generalist, and object-aware teachers, encouraging a shared embedding space that captures signals from high-level semantics to fine-grained structure. We evaluate Chorus on a wide range of tasks: open-vocabulary semantic and instance segmentation, linear and decoder probing, as well as data-efficient supervision. Besides 3DGS, we also test Chorus on several benchmarks that only support point clouds by pretraining a variant using only Gaussians' centers, colors, estimated normals as inputs. Interestingly, this encoder shows strong transfer and outperforms the point clouds baseline while using 39.9 times fewer training scenes. Finally, we propose a render-and-distill adaptation that facilitates out-of-domain finetuning. Our code and model will be released upon publication.
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。
