Foundation Model assisted Weakly Supervised LiDAR Semantic Segmentation

Current point cloud semantic segmentation has achieved great advances when given sufficient labels. However, the dense annotation of LiDAR point clouds remains prohibitively expensive and time-consuming, unable to keep up with the continuously growing volume of data. In this paper, we propose annotating images with scattered points, followed by utilizing SAM (a Foundation model) to generate semantic segmentation labels for the images. Finally, by mapping the segmentation labels of the images to the LiDAR space using the intrinsic and extrinsic parameters of the camera and LiDAR, we obtain labels for point cloud semantic segmentation, and release Scatter-KITTI and Scatter-nuScenes, which are the first works to utilize image segmentation-based SAM for weakly supervised point cloud semantic segmentation. Furthermore, to mitigate the influence of erroneous pseudo labels obtained from sparse annotations on point cloud features, we propose a multi-modal weakly supervised network for LiDAR semantic segmentation, called MM-ScatterNet. This network combines features from both point cloud and image modalities, enhancing the representation learning of point clouds by introducing consistency constraints between multi-modal features and point cloud features. On the SemanticKITTI dataset, we achieve 66\% of fully supervised performance using only 0.02% of annotated data, and on the NuScenes dataset, we achieve 95% of fully supervised performance using only 0.1% labeled points.

Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentation

Impressive performance on point cloud semantic segmentation has been achieved by fully-supervised methods with large amounts of labelled data. As it is labour-intensive to acquire large-scale point cloud data with point-wise labels, many attempts have been made to explore learning 3D point cloud segmentation with limited annotations. Active learning is one of the effective strategies to achieve this purpose but is still under-explored. The most recent methods of this kind measure the uncertainty of each pre-divided region for manual labelling but they suffer from redundant information and require additional efforts for region division. This paper aims at addressing this issue by developing a hierarchical point-based active learning strategy. Specifically, we measure the uncertainty for each point by a hierarchical minimum margin uncertainty module which considers the contextual information at multiple levels. Then, a feature-distance suppression strategy is designed to select important and representative points for manual labelling. Besides, to better exploit the unlabelled data, we build a semi-supervised segmentation framework based on our active strategy. Extensive experiments on the S3DIS and ScanNetV2 datasets demonstrate that the proposed framework achieves 96.5% and 100% performance of fully-supervised baseline with only 0.07% and 0.1% training data, respectively, outperforming the state-of-the-art weakly-supervised and active learning methods. The code will be available at https://github.com/SmiletoE/HPAL.

GenReg: Deep Generative Method for Fast Point Cloud Registration

Accurate and efficient point cloud registration is a challenge because the noise and a large number of points impact the correspondence search. This challenge is still a remaining research problem since most of the existing methods rely on correspondence search. To solve this challenge, we propose a new data-driven registration algorithm by investigating deep generative neural networks to point cloud registration. Given two point clouds, the motivation is to generate the aligned point clouds directly, which is very useful in many applications like 3D matching and search. We design an end-to-end generative neural network for aligned point clouds generation to achieve this motivation, containing three novel components. Firstly, a point multi-perception layer (MLP) mixer (PointMixer) network is proposed to efficiently maintain both the global and local structure information at multiple levels from the self point clouds. Secondly, a feature interaction module is proposed to fuse information from cross point clouds. Thirdly, a parallel and differential sample consensus method is proposed to calculate the transformation matrix of the input point clouds based on the generated registration results. The proposed generative neural network is trained in a GAN framework by maintaining the data distribution and structure similarity. The experiments on both ModelNet40 and 7Scene datasets demonstrate that the proposed algorithm achieves state-of-the-art accuracy and efficiency. Notably, our method reduces $2\times$ in registration error (CD) and $12\times$ running time compared to the state-of-the-art correspondence-based algorithm.