Simultaneous Localization and Mapping (SLAM) with dense representation plays
a key role in robotics, Virtual Reality (VR), and Augmented Reality (AR)
applications. Recent advancements in dense representation SLAM have highlighted
the potential of leveraging neural scene representation and 3D Gaussian
representation for high-fidelity spatial representation. In this paper, we
propose a novel dense representation SLAM approach with a fusion of Generalized
Iterative Closest Point (G-ICP) and 3D Gaussian Splatting (3DGS). In contrast
to existing methods, we utilize a single Gaussian map for both tracking and
mapping, resulting in mutual benefits. Through the exchange of covariances
between tracking and mapping processes with scale alignment techniques, we
minimize redundant computations and achieve an efficient system. Additionally,
we enhance tracking accuracy and mapping quality through our keyframe selection
methods. Experimental results demonstrate the effectiveness of our approach,
showing an incredibly fast speed up to 107 FPS (for the entire system) and
superior quality of the reconstructed map.

提出了一种融合通用迭代最近点（G-ICP）和 3D 高斯光滑（3DGS）的新型密集表达 SLAM 方法，通过使用单个高斯地图进行跟踪和建图，实现了冗余计算的最小化、系统效率的提升以及跟踪精度和建图质量的增强。

RGBD GS-ICP SLAM

In this paper, we propose an adaptive weighting regression (AWR) method to
leverage the advantages of both detection-based and regression-based methods.
Hand joint coordinates are estimated as discrete integration of all pixels in
dense representation, guided by adaptive weight maps. This learnable
aggregation process introduces both dense and joint supervision that allows
end-to-end training and brings adaptability to weight maps, making the network
more accurate and robust. Comprehensive exploration experiments are conducted
to validate the effectiveness and generality of AWR under various experimental
settings, especially its usefulness for different types of dense representation
and input modality. Our method outperforms other state-of-the-art methods on
four publicly available datasets, including NYU, ICVL, MSRA and HANDS 2017
dataset.

本文提出了一种自适应加权回归方法，并且结合密集表示和联合监督学习，实现端到端训练和提高模型的准确度和鲁棒性，在四个公开数据集上的表现比其他最先进方法更好。

AWR: 自适应权重回归用于三维手势估计

AWR: Adaptive Weighting Regression for 3D Hand Pose Estimation

The representation of geometry in real-time 3D perception systems continues
to be a critical research issue. Dense maps capture complete surface shape and
can be augmented with semantic labels, but their high dimensionality makes them
computationally costly to store and process, and unsuitable for rigorous
probabilistic inference. Sparse feature-based representations avoid these
problems, but capture only partial scene information and are mainly useful for
localisation only.
We present a new compact but dense representation of scene geometry which is
conditioned on the intensity data from a single image and generated from a code
consisting of a small number of parameters. We are inspired by work both on
learned depth from images, and auto-encoders. Our approach is suitable for use
in a keyframe-based monocular dense SLAM system: While each keyframe with a
code can produce a depth map, the code can be optimised efficiently jointly
with pose variables and together with the codes of overlapping keyframes to
attain global consistency. Conditioning the depth map on the image allows the
code to only represent aspects of the local geometry which cannot directly be
predicted from the image. We explain how to learn our code representation, and
demonstrate its advantageous properties in monocular SLAM.

本文介绍了一种基于单张图像强度数据的密集场景几何表示方法，在单目稠密 SLAM 系统中具有优异的性能表现。