RESUMEN
In recent years, vision-centric Bird's Eye View (BEV) perception has garnered significant interest from both industry and academia due to its inherent advantages, such as providing an intuitive representation of the world and being conducive to data fusion. The rapid advancements in deep learning have led to the proposal of numerous methods for addressing vision-centric BEV perception challenges. However, there has been no recent survey encompassing this novel and burgeoning research field. To catalyze future research, this paper presents a comprehensive survey of the latest developments in vision-centric BEV perception and its extensions. It compiles and organizes up-to-date knowledge, offering a systematic review and summary of prevalent algorithms. Additionally, the paper provides in-depth analyses and comparative results on various BEV perception tasks, facilitating the evaluation of future works and sparking new research directions. Furthermore, the paper discusses and shares valuable empirical implementation details to aid in the advancement of related algorithms.
RESUMEN
With the rapid advances in autonomous driving, it becomes critical to equip its sensing system with more holistic 3D perception. However, widely explored tasks like 3D detection or point cloud semantic segmentation focus on parsing either the objects or scenes. In this work, we propose to address the challenging task of LiDAR-based Panoptic Segmentation, which aims to parse both objects and scenes in a unified manner. In particular, we propose Dynamic Shifting Network (DS-Net), which serves as an effective panoptic segmentation framework in the point cloud realm. DS-Net features a dynamic shifting module for complex LiDAR point cloud distributions. We present an efficient learnable clustering module, dynamic shifting, which adapts kernel functions for different instances. To further explore the temporal information, we extend the single-scan processing framework to its temporal version, 4D-DS-Net, for the task of 4D Panoptic Segmentation, where the same instance across multiple frames should be given the same ID prediction. Instead of naively appending a tracking module to DS-Net, we propose to solve the 4D panoptic segmentation in a more unified way. Specifically, 4D-DS-Net first constructs 4D data volume by aligning consecutive LiDAR scans, upon which the temporally unified instance clustering is performed to obtain the final results. Extensive experiments on two large-scale autonomous driving LiDAR datasets, SemanticKITTI and Panoptic nuScenes, are conducted to demonstrate the effectiveness and superior performance of the proposed solution.
RESUMEN
State-of-the-art methods for driving-scene LiDAR-based perception (including point cloud semantic segmentation, panoptic segmentation and 3D detection, etc.) often project the point clouds to 2D space and then process them via 2D convolution. Although this cooperation shows the competitiveness in the point cloud, it inevitably alters and abandons the 3D topology and geometric relations. A natural remedy is to utilize the 3D voxelization and 3D convolution network. However, we found that in the outdoor point cloud, the improvement obtained in this way is quite limited. An important reason is the property of the outdoor point cloud, namely sparsity and varying density. Motivated by this investigation, we propose a new framework for the outdoor LiDAR segmentation, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern while maintaining these inherent properties. The proposed model acts as a backbone and the learned features from this model can be used for downstream tasks such as point cloud semantic and panoptic segmentation or 3D detection. In this paper, we benchmark our model on these three tasks. For semantic segmentation, we evaluate the proposed model on several large-scale datasets, i.e., SemanticKITTI, nuScenes and A2D2. Our method achieves the state-of-the-art on the leaderboard of SemanticKITTI (both single-scan and multi-scan challenge), and significantly outperforms existing methods on nuScenes and A2D2 dataset. Furthermore, the proposed 3D framework also shows strong performance and good generalization on LiDAR panoptic segmentation and LiDAR 3D detection.
