PointTransformerX: Portable and Efficient 3D Point Cloud Processing without Sparse Algorithms
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917446790676480 |
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| author | Reichardt, Laurenz Ebert, Nikolas Wasenmüller, Oliver |
| author_facet | Reichardt, Laurenz Ebert, Nikolas Wasenmüller, Oliver |
| contents | 3D point cloud perception remains tightly coupled to custom CUDA operators for spatial operations, limiting portability and efficiency on non-NVIDIA, AMD, and embedded hardware. We introduce PointTransformerX (PTX), a fully PyTorch-native vision transformer backbone for 3D point clouds, removing all custom CUDA operators and external libraries while retaining competitive accuracy. PTX introduces 3D-GS-RoPE, a rotary positional embedding that encodes 3D spatial relationships directly in self-attention without neighborhood construction, and further replaces sparse convolutional patch embedding with a linear projection. PTX explores inference-time scaling of attention windows to improve accuracy without retraining. With a redesigned feed-forward network, PTX achieves 98.7\% of PointTransformer V3's accuracy on ScanNet with 79.2\% fewer parameters and executing 1.6\times faster while requiring just 253 MB memory. PTX runs natively on NVIDIA GPUs, AMD GPUs (ROCm), and CPUs, providing an efficient and portable foundation for point cloud perception. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24169 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PointTransformerX: Portable and Efficient 3D Point Cloud Processing without Sparse Algorithms Reichardt, Laurenz Ebert, Nikolas Wasenmüller, Oliver Computer Vision and Pattern Recognition 3D point cloud perception remains tightly coupled to custom CUDA operators for spatial operations, limiting portability and efficiency on non-NVIDIA, AMD, and embedded hardware. We introduce PointTransformerX (PTX), a fully PyTorch-native vision transformer backbone for 3D point clouds, removing all custom CUDA operators and external libraries while retaining competitive accuracy. PTX introduces 3D-GS-RoPE, a rotary positional embedding that encodes 3D spatial relationships directly in self-attention without neighborhood construction, and further replaces sparse convolutional patch embedding with a linear projection. PTX explores inference-time scaling of attention windows to improve accuracy without retraining. With a redesigned feed-forward network, PTX achieves 98.7\% of PointTransformer V3's accuracy on ScanNet with 79.2\% fewer parameters and executing 1.6\times faster while requiring just 253 MB memory. PTX runs natively on NVIDIA GPUs, AMD GPUs (ROCm), and CPUs, providing an efficient and portable foundation for point cloud perception. |
| title | PointTransformerX: Portable and Efficient 3D Point Cloud Processing without Sparse Algorithms |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.24169 |