CVSformer: Cross-View Synthesis Transformer for Semantic Scene Completion

Dong, Haotian; Ma, Enhui; Wang, Lubo; Wang, Miaohui; Xie, Wuyuan; Guo, Qing; Li, Ping; Liang, Lingyu; Yang, Kairui; Lin, Di

doi:10.1109/iccv51070.2023.00815

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2024

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Cited by 2 publications

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Unleashing Network Potentials for Semantic Scene Completion

Wang,

Sun,

Zhang

et al. 2024

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Unleashing Network Potentials for Semantic Scene Completion

Wang,

Sun,

Zhang

et al. 2024

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Voxel- and Bird’s-Eye-View-Based Semantic Scene Completion for LiDAR Point Clouds

Liang,

Akhtar,

Vice

et al. 2024

Remote Sensing

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Semantic scene completion is a crucial outdoor scene understanding task that has direct implications for technologies like autonomous driving and robotics. It compensates for unavoidable occlusions and partial measurements in LiDAR scans, which may otherwise cause catastrophic failures. Due to the inherent complexity of this task, existing methods generally rely on complex and computationally demanding scene completion models, which limits their practicality in downstream applications. Addressing this, we propose a novel integrated network that combines the strengths of 3D and 2D semantic scene completion techniques for efficient LiDAR point cloud scene completion. Our network leverages a newly devised lightweight multi-scale convolutional block (MSB) to efficiently aggregate multi-scale features, thereby improving the identification of small and distant objects. It further utilizes a layout-aware semantic block (LSB), developed to grasp the overall layout of the scene to precisely guide the reconstruction and recognition of features. Moreover, we also develop a feature fusion module (FFM) for effective interaction between the data derived from two disparate streams in our network, ensuring a robust and cohesive scene completion process. Extensive experiments with the popular SemanticKITTI dataset demonstrate that our method achieves highly competitive performance, with an mIoU of 35.7 and an IoU of 51.4. Notably, the proposed method achieves an mIoU improvement of 2.6 % compared to previous methods.

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CVSformer: Cross-View Synthesis Transformer for Semantic Scene Completion

Cited by 2 publications

References 36 publications

Unleashing Network Potentials for Semantic Scene Completion

Unleashing Network Potentials for Semantic Scene Completion

Voxel- and Bird’s-Eye-View-Based Semantic Scene Completion for LiDAR Point Clouds

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