Developing a Voxel-Based Semantic Segmentation Technology for Human Behavior Representation in an Atypical Architectural Design
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62110-9_34
Reference5 articles.
1. Wang, P., et al.: Understanding convolution for semantic segmentation. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, pp. 1451–1460 (2018)
2. Straub, J., et al.: The Replica Dataset: A Digital Replica of Indoor Spaces (2019)
3. Jung, S., Lee, J., Gwak, D., Choi, S., Choo, J.: Standardized max logits: a simple yet effective approach for identifying unexpected road obstacles in urban-scene segmentation (2021)
4. Tan, W., et al.: Toronto-3D: a large-scale mobile LiDAR dataset for semantic segmentation of urban roadways (2020)
5. Chen, Z., et al.: PointDC: unsupervised semantic segmentation of 3D point clouds via cross-modal distillation and super-voxel clustering (2023)
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