用物体先验实现在线3D场景重建，精度与完整性更优。
Online 3D Scene Reconstruction Using Neural Object Priors
- 通过特征网格插值动态更新物体中心的神经隐式表示
- 在真实和合成数据上重建精度优于现有模型，完整度提升显著
- 适合需要实时高精度3D重建的应用，如机器人导航
本文解决基于RGB-D视频序列在线重建场景中物体级结构的问题。尽管当前物体感知的神经隐式表示具有潜力，但在在线重建效率和形状补全方面仍受限。为此，我们提出两项改进：首先，设计特征网格插值机制，持续更新基于网格的物体中心神经隐式表示，以适应新暴露的物体部分；其次，预先构建包含已映射物体的物体库，利用其形状先验初始化新视频中的几何物体模型，并通过新视角与合成历史视角完成细节恢复，避免丢失原始信息。在Replica合成环境、ScanNet真实场景及实验室采集视频上的大量实验表明，该方法在重建精度与完整性上均优于当前最先进神经隐式模型。
原文摘要 · Abstract (English)
This paper addresses the problem of reconstructing a scene online at the level of objects given an RGB-D video sequence. While current object-aware neural implicit representations hold promise, they are limited in online reconstruction efficiency and shape completion. Our main contributions to alleviate the above limitations are twofold. First, we propose a feature grid interpolation mechanism to continuously update grid-based object-centric neural implicit representations as new object parts are revealed. Second, we construct an object library with previously mapped objects in advance and leverage the corresponding shape priors to initialize geometric object models in new videos, subsequently completing them with novel views as well as synthesized past views to avoid losing original object details. Extensive experiments on synthetic environments from the Replica dataset, real-world ScanNet sequences and videos captured in our laboratory demonstrate that our approach outperforms state-of-the-art neural implicit models for this task in terms of reconstruction accuracy and completeness.
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