Implicit Neural Representations have gained prominence as a powerful
framework for capturing complex data modalities, encompassing a wide range from
3D shapes to images and audio. Within the realm of 3D shape representation,
Neural Signed Distance Functions (SDF) have demonstrated remarkable potential
in faithfully encoding intricate shape geometry. However, learning SDFs from 3D
point clouds in the absence of ground truth supervision remains a very
challenging task. In this paper, we propose a method to infer occupancy fields
instead of SDFs as they are easier to learn from sparse inputs. We leverage a
margin-based uncertainty measure to differentially sample from the decision
boundary of the occupancy function and supervise the sampled boundary points
using the input point cloud. We further stabilize the optimization process at
the early stages of the training by biasing the occupancy function towards
minimal entropy fields while maximizing its entropy at the input point cloud.
Through extensive experiments and evaluations, we illustrate the efficacy of
our proposed method, highlighting its capacity to improve implicit shape
inference with respect to baselines and the state-of-the-art using synthetic
and real data.

通过使用基于边界采样的方法和基于熵的优化过程，我们提出一种从稀疏输入中学习占据场的方法，并展示了该方法在隐式形状推断方面相对于基线方法和现有技术的有效性。

基于稀疏点云的无监督占据学习

Unsupervised Occupancy Learning from Sparse Point Cloud

Collision detection is one of the most time-consuming operations during
motion planning. Thus, there is an increasing interest in exploring machine
learning techniques to speed up collision detection and sampling-based motion
planning. A recent line of research focuses on utilizing neural signed distance
functions of either the robot geometry or the swept volume of the robot motion.
Building on this, we present a novel neural implicit swept volume model that is
the first to continuously represent arbitrary motions parameterized by their
start and goal configurations. This allows to quickly compute signed distances
for any point in the task space to the robot motion. Further, we present an
algorithm combining the speed of the deep learning-based signed distance
computations with the strong accuracy guarantees of geometric collision
checkers. We validate our approach in simulated and real-world robotic
experiments, and demonstrate that it is able to speed up a commercial bin
picking application.

通过利用神经隐式扫描体模型，结合深度学习方法快速计算任务空间中任意点与机器人运动之间的有向距离，并结合几何碰撞检测器提供强大的精确性保证，实现了对商业物料拾取应用的加速。

神经隐式扫描体模型用于快速碰撞检测

Neural Implicit Swept Volume Models for Fast Collision Detection

We investigate the generalization capabilities of neural signed distance
functions (SDFs) for learning 3D object representations for unseen and
unlabeled point clouds. Existing methods can fit SDFs to a handful of object
classes and boast fine detail or fast inference speeds, but do not generalize
well to unseen shapes. We introduce a two-stage semi-supervised meta-learning
approach that transfers shape priors from labeled to unlabeled data to
reconstruct unseen object categories. The first stage uses an episodic training
scheme to simulate training on unlabeled data and meta-learns initial shape
priors. The second stage then introduces unlabeled data with disjoint classes
in a semi-supervised scheme to diversify these priors and achieve
generalization. We assess our method on both synthetic data and real collected
point clouds. Experimental results and analysis validate that our approach
outperforms existing neural SDF methods and is capable of robust zero-shot
inference on 100+ unseen classes. Code can be found at
this https URL

本文研究了使用神经代表距离函数（SDF）进行三维对象表示学习的泛化能力，提出了一个半监督元学习方法，可将形状先验从有标签的数据传输到无标签的数据中，用于重建未知的对象类别，并实现了 100 多个未见过的类别的强大的零 - shot 推理。