3D occupancy and scene flow offer a detailed and dynamic representation of 3D scene. Recognizing the sparsity and complexity of 3D space, previous vision-centric methods have employed implicit learning-based approaches to model spatial and temporal information. However, these approaches struggle to capture local details and diminish the model’s spatial discriminative ability. To address these challenges, we propose a novel explicit state-based modeling method designed to leverage the occupied state to renovate the 3D features. Specifically, we propose a sparse occlusion-aware attention mechanism, integrated with a cascade refinement strategy, which accurately renovates 3D features with the guidance of occupied state information. Additionally, we introduce a novel method for modeling long-term dynamic interactions, which reduces computational costs and preserves spatial information. Compared to the previous state-of-the-art methods, our efficient explicit renovation strategy not only delivers superior performance in terms of RayloU and mAVE for occupancy and scene flow prediction but also markedly reduces GPU memory usage during training, bringing it down to 8.7GB. Our code is available on https://github.com/lzzzzzm/STCOcc

LiDAR-based point cloud segmentation is a significant and challenging task for 3D scene understanding. Recent voxel-based methods are often built on submanifold sparse residual calculation with small kernel size, which limits the local feature interactions and neglects the global contextual information. In this paper, we propose a local-to-global perception LiDAR-based point cloud segmentation network LGPSeg. From the local perspective, we design the Dynamic Spatial Aggregation Convolution to expand the receptive field range while avoiding a large increase in the model parameters. From the global perspective, we propose the BEV-Voxel Fusion to aggregate the global contextual information on the BEV feature maps through advanced 2D operators. By combining the local and global features, the 3D object and background information can be better captured. Our method achieves state-of-the-art results on two large datasets, SemanticKITTI and nuScenes, and even outperformed multimodal-based methods.