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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.14024 |
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| _version_ | 1866911218594217984 |
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| author | Uss, Mykhailo Yermolenko, Ruslan Shashko, Oleksii Kolodiazhna, Olena Safonov, Ivan Savin, Volodymyr Yeo, Yoonjae Ji, Seowon Jeong, Jaeyun |
| author_facet | Uss, Mykhailo Yermolenko, Ruslan Shashko, Oleksii Kolodiazhna, Olena Safonov, Ivan Savin, Volodymyr Yeo, Yoonjae Ji, Seowon Jeong, Jaeyun |
| contents | Dense depth prediction deep neural networks (DNN) have achieved impressive results for both monocular and binocular data, but still they are limited by high computational complexity, restricting their use on low-end devices. For better on-device efficiency and hardware utilization, weights and activations of the DNN should be converted to low-bit precision. However, this precision is not sufficient to represent high dynamic range depth. In this paper, we aim to overcome this limitation and restore high-precision depth from low-bit precision predictions. To achieve this, we propose to represent high dynamic range depth as two low dynamic range components of a Hilbert curve, and to train the full-precision DNN to directly predict the latter. For on-device deployment, we use standard quantization methods and add a post-processing step that reconstructs depth from the Hilbert curve components predicted in low-bit precision. Extensive experiments demonstrate that our method increases the bit precision of predicted depth by up to three bits with little computational overhead. We also observed a positive side effect of quantization error reduction by up to 4.6 times. Our method enables effective and accurate depth prediction with DNN weights and activations quantized to eight-bit precision. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_14024 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predicting High-precision Depth on Low-Precision Devices Using 2D Hilbert Curves Uss, Mykhailo Yermolenko, Ruslan Shashko, Oleksii Kolodiazhna, Olena Safonov, Ivan Savin, Volodymyr Yeo, Yoonjae Ji, Seowon Jeong, Jaeyun Computer Vision and Pattern Recognition Artificial Intelligence Dense depth prediction deep neural networks (DNN) have achieved impressive results for both monocular and binocular data, but still they are limited by high computational complexity, restricting their use on low-end devices. For better on-device efficiency and hardware utilization, weights and activations of the DNN should be converted to low-bit precision. However, this precision is not sufficient to represent high dynamic range depth. In this paper, we aim to overcome this limitation and restore high-precision depth from low-bit precision predictions. To achieve this, we propose to represent high dynamic range depth as two low dynamic range components of a Hilbert curve, and to train the full-precision DNN to directly predict the latter. For on-device deployment, we use standard quantization methods and add a post-processing step that reconstructs depth from the Hilbert curve components predicted in low-bit precision. Extensive experiments demonstrate that our method increases the bit precision of predicted depth by up to three bits with little computational overhead. We also observed a positive side effect of quantization error reduction by up to 4.6 times. Our method enables effective and accurate depth prediction with DNN weights and activations quantized to eight-bit precision. |
| title | Predicting High-precision Depth on Low-Precision Devices Using 2D Hilbert Curves |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2405.14024 |