Implicit reconstruction of ESDF (Euclidean Signed Distance Field) involves
training a neural network to regress the signed distance from any point to the
nearest obstacle, which has the advantages of lightweight storage and
continuous querying. However, existing algorithms usually rely on conflicting
raw observations as training data, resulting in poor map performance. In this
paper, we propose LGSDF, an ESDF continual Global learning algorithm aided by
Local updating. At the front end, axis-aligned grids are dynamically updated by
pre-processed sensor observations, where incremental fusion alleviates
estimation error caused by limited viewing directions. At the back end, a
randomly initialized implicit ESDF neural network performs continual
self-supervised learning guided by these grids to generate smooth and
continuous maps. The results on multiple scenes show that LGSDF can construct
more accurate ESDF maps and meshes compared with SOTA (State Of The Art)
explicit and implicit mapping algorithms. The source code of LGSDF is publicly
available at this https URL

该论文提出了一种 LGSDF 算法，通过动态更新轴对齐网格和持续自监督学习的神经网络来实现 ESDF 的隐式重建，从而构建更准确的 ESDF 地图和网格。

LGSDF: 基于局部更新的连续全局有符号距离场学习

LGSDF: Continual Global Learning of Signed Distance Fields Aided by  Local Updating

We introduce a novel and general loss function, called Symmetric Contrastive
(Sy-CON) loss, for effective continual self-supervised learning (CSSL). We
first argue that the conventional loss form of continual learning which
consists of single task-specific loss (for plasticity) and a regularizer (for
stability) may not be ideal for contrastive loss based CSSL that focus on
representation learning. Our reasoning is that, in contrastive learning based
methods, the task-specific loss would suffer from decreasing diversity of
negative samples and the regularizer may hinder learning new distinctive
representations. To that end, we propose Sy-CON that consists of two losses
(one for plasticity and the other for stability) with symmetric dependence on
current and past models' negative sample embeddings. We argue our model can
naturally find good trade-off between the plasticity and stability without any
explicit hyperparameter tuning. We validate the effectiveness of our approach
through extensive experiments, demonstrating that MoCo-based implementation of
Sy-CON loss achieves superior performance compared to other state-of-the-art
CSSL methods.

引入了一种新型通用损失函数 Symmetric Contrastive (Sy-CON) loss 用于有效地连续自监督学习 (CSSL).