Xingyi Li

Yizheng Wu, Zhiyu Pan, Kewei Wang et al.

Large-scale datasets with point-wise semantic and instance labels are crucial to 3D instance segmentation but also expensive. To leverage unlabeled data, previous semi-supervised 3D instance segmentation approaches have explored self-training frameworks, which rely on high-quality pseudo labels for consistency regularization. They intuitively utilize both instance and semantic pseudo labels in a joint learning manner. However, semantic pseudo labels contain numerous noise derived from the imbalanced category distribution and natural confusion of similar but distinct categories, which leads to severe collapses in self-training. Motivated by the observation that 3D instances are non-overlapping and spatially separable, we ask whether we can solely rely on instance consistency regularization for improved semi-supervised segmentation. To this end, we propose a novel self-training network InsTeacher3D to explore and exploit pure instance knowledge from unlabeled data. We first build a parallel base 3D instance segmentation model DKNet, which distinguishes each instance from the others via discriminative instance kernels without reliance on semantic segmentation. Based on DKNet, we further design a novel instance consistency regularization framework to generate and leverage high-quality instance pseudo labels. Experimental results on multiple large-scale datasets show that the InsTeacher3D significantly outperforms prior state-of-the-art semi-supervised approaches. Code is available: https://github.com/W1zheng/InsTeacher3D.

Xingyi Li, Han Zhang, Ziliang Wang et al.

Point cloud registration is a key step in robotic perception tasks, such as Simultaneous Localization and Mapping (SLAM). It is especially challenging in conditions with sparse points and heavy noise. Traditional registration methods, such as Iterative Closest Point (ICP) and Normal Distributions Transform (NDT), often have difficulties in achieving a robust and accurate alignment under these conditions. In this paper, we propose a registration framework based on moment matching. In particular, the point clouds are regarded as i.i.d. samples drawn from the same distribution observed in the source and target frames. We then match the generalized Gaussian Radial Basis moments calculated from the point clouds to estimate the rigid transformation between two frames. Moreover, such method does not require explicit point-to-point correspondences among the point clouds. We further show the consistency of the proposed method. Experiments on synthetic and real-world datasets show that our approach achieves higher accuracy and robustness than existing methods. In addition, we integrate our framework into a 4D Radar SLAM system. The proposed method significantly improves the localization performance and achieves results comparable to LiDAR-based systems. These findings demonstrate the potential of moment matching technique for robust point cloud registration in sparse and noisy scenarios.

Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern et al.

Recently, there has been a significant interest in performing convolution over irregularly sampled point clouds. Since point clouds are very different from regular raster images, it is imperative to study the generalization of the convolution networks more closely, especially their robustness under variations in scale and rotations of the input data. This paper investigates different variants of PointConv, a convolution network on point clouds, to examine their robustness to input scale and rotation changes. Of the variants we explored, two are novel and generated significant improvements. The first is replacing the multilayer perceptron based weight function with much simpler third degree polynomials, together with a Sobolev norm regularization. Secondly, for 3D datasets, we derive a novel viewpoint-invariant descriptor by utilizing 3D geometric properties as the input to PointConv, in addition to the regular 3D coordinates. We have also explored choices of activation functions, neighborhood, and subsampling methods. Experiments are conducted on the 2D MNIST & CIFAR-10 datasets as well as the 3D SemanticKITTI & ScanNet datasets. Results reveal that on 2D, using third degree polynomials greatly improves PointConv's robustness to scale changes and rotations, even surpassing traditional 2D CNNs for the MNIST dataset. On 3D datasets, the novel viewpoint-invariant descriptor significantly improves the performance as well as robustness of PointConv. We achieve the state-of-the-art semantic segmentation performance on the SemanticKITTI dataset, as well as comparable performance with the current highest framework on the ScanNet dataset among point-based approaches.