Fast and efficient semantic segmentation of large-scale LiDAR point clouds is a fundamental problem in autonomous driving. To achieve this goal, the existing point-based methods mainly choose to adopt Random Sampling strategy to process large-scale point clouds. However, our quantative and qualitative studies have found that Random Sampling may be less suitable for the autonomous driving scenario, since the LiDAR points follow an uneven or even long-tailed distribution across the space, which prevents the model from capturing sufficient information from points in different distance ranges and reduces the model’s learning capability. To alleviate this problem, we propose a new Polar Cylinder Balanced Random Sampling method that enables the downsampled point clouds to maintain a more balanced distribution and improve the segmentation performance under different spatial distributions. In addition, a sampling consistency loss is introduced to further improve the segmentation performance and reduce the model’s variance under different sampling methods. Extensive experiments confirm that our approach produces excellent performance on both SemanticKITTI and SemanticPOSS benchmarks, achieving a 2.8% and 4.0% improvement, respectively. The source code is available at PCB-RandNet.

The current 3D object detection datasets suffer from a serve problem of uneven data distribution. In autonomous driving scenarios, rare classes are also critical. The existing methods perform poorly in these classes. Generic methods of solving long-tailed distributions such as re-sampling and re-weighting would over-fit or destroy the original distribution. The popular solution is to design detectors for different classes, which improves detection accuracy but reduces generalizability. Motived by this, we propose a simple and effective single-stage detector named CA-SSD, which can detect multiple categories simultaneously. The key to our approach is the channel transformation of the features, which corrects for the bias in task distribution from the training phase to the testing phase. In addition, to reduce the information loss due to multiple downsampling, a self-attention module is introduced to retain richer contextual information. Experimental results on the KITTI benchmark demonstrate the superiority of CA-SSD, especially on small samples. Besides, we designed ablation experiments to verify the effectiveness of both modules.

Effective and efficient 3D semantic segmentation from large-scale LiDAR point cloud is a fundamental problem in the field of autonomous driving. In this paper, we present Transformer-Range-View Network (TransRVNet), a novel and powerful projection-based CNN-Transformer architecture to infer point-wise semantics. First, a Multi Residual Channel Interaction Attention Module (MRCIAM) is introduced to capture channel-level multi-scale feature and model intra-channel, inter-channel correlations based on attention mechanism. Then, in the encoder stage, we use a well-designed Residual Context Aggregation Module (RCAM), including a residual dilated convolution structure and a context aggregation module, to fuse information from different receptive fields while reducing the impact of missing points. Finally, a Balanced Non-square-Transformer Module (BNTM) is employed as fundamental component of decoder to achieve locally feature dependencies for more discriminative feature learning by introducing the non-square shifted window strategy. Extensive qualitative and quantitative experiments conducted on challenging SemanticKITTI and SemanticPOSS benchmarks have verified the effectiveness of our proposed technique. Our TransRVNet presents superior performance over most existing state-of-the-art approaches. The source code and trained model are available athttps://github.com/huixiancheng/TransRVNet.

Feature representation learning is a key component in 3D point cloud analysis. However, the powerful convolutional neural networks (CNNs) cannot be applied due to the irregular structure of point clouds. Therefore, following the tremendous success of transformer in natural language processing and image understanding tasks, in this paper, we present a novel point cloud representation learning architecture, named Dual Transformer Network (DTNet), which mainly consists of Dual Point Cloud Transformer (DPCT) module. Specifically, by aggregating the well-designed point-wise and channel-wise self-attention models simultaneously, DPCT module can capture much richer contextual dependencies semantically from the perspective of position and channel. With the DPCT model as a fundamental component, we construct the DTNet for performing 3D point cloud analysis in an end-to-end manner. Extensive quantitative and qualitative experiments on publicly available benchmarks demonstrate the effectiveness of our transformer framework for the tasks of 3D point cloud classification, segmentation and visual object affordance understanding, achieving highly competitive performance in comparison with the state-of-the-art approaches.

Accurate and fast scene understanding is one of the chal-lenging task for autonomous driving, which requires to take full advantage of LiDAR point clouds for semantic segmen-tation. In this paper, we present a concise and efficient image-based semantic segmentation network, named CENet. In order to improve the descriptive power of learned features and reduce the computational as well as time complex-ity, our CEN et integrates the convolution with larger ker-nel size instead of MLP, carefully-selected activation functions, and multiple auxiliary segmentation heads with cor-responding loss functions into architecture. Quantitative and qualitative experiments conducted on publicly available benchmarks, SemanticKITTI and SemanticPOSS, demon-strate that our pipeline achieves much better mIoU and in-ference performance compared with state-of-the-art models. The code will be available at https://github.com/huixiancheng/CENet.