Volumetric models have become a popular representation for 3D scenes in
recent years. One breakthrough leading to their popularity was KinectFusion,
which focuses on 3D reconstruction using RGB-D sensors. However, monocular SLAM
has since also been tackled with very similar approaches. Representing the
reconstruction volumetrically as a TSDF leads to most of the simplicity and
efficiency that can be achieved with GPU implementations of these systems.
However, this representation is memory-intensive and limits applicability to
small-scale reconstructions. Several avenues have been explored to overcome
this. With the aim of summarizing them and providing for a fast, flexible 3D
reconstruction pipeline, we propose a new, unifying framework called InfiniTAM.
The idea is that steps like camera tracking, scene representation and
integration of new data can easily be replaced and adapted to the user's needs.
This report describes the technical implementation details of InfiniTAM v3,
the third version of our InfiniTAM system. We have added various new features,
as well as making numerous enhancements to the low-level code that
significantly improve our camera tracking performance. The new features that we
expect to be of most interest are (i) a robust camera tracking module; (ii) an
implementation of Glocker et al.'s keyframe-based random ferns camera
relocaliser; (iii) a novel approach to globally-consistent TSDF-based
reconstruction, based on dividing the scene into rigid submaps and optimising
the relative poses between them; and (iv) an implementation of Keller et al.'s
surfel-based reconstruction approach.

本次报告介绍了 InfiniTAM 系统的第三个版本，主要介绍了其独特的处理相机跟踪、场景表示和新数据集成的灵活性以及 TSDF 方法的全局一致性最佳实践。

InfiniTAM v3: 大规模三维重建框架与回路闭合

InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop  Closure

Volumetric models have become a popular representation for 3D scenes in
recent years. One of the breakthroughs leading to their popularity was
KinectFusion, where the focus is on 3D reconstruction using RGB-D sensors.
However, monocular SLAM has since also been tackled with very similar
approaches. Representing the reconstruction volumetrically as a truncated
signed distance function leads to most of the simplicity and efficiency that
can be achieved with GPU implementations of these systems. However, this
representation is also memory-intensive and limits the applicability to small
scale reconstructions. Several avenues have been explored for overcoming this
limitation. With the aim of summarizing them and providing for a fast and
flexible 3D reconstruction pipeline, we propose a new, unifying framework
called InfiniTAM. The core idea is that individual steps like camera tracking,
scene representation and integration of new data can easily be replaced and
adapted to the needs of the user. Along with the framework we also provide a
set of components for scalable reconstruction: two implementations of camera
trackers, based on RGB data and on depth data, two representations of the 3D
volumetric data, a dense volume and one based on hashes of subblocks, and an
optional module for swapping subblocks in and out of the typically limited GPU
memory.

InfiniTAM 是一个新的统一框架，它可以快速灵活地进行 3D 重建，基于 KinectFusion 方法和单目 SLAM 方法，使用截断有符号距离函数来表示体积模型，提供了一系列组件来实现可扩展的 3D 重建。