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| Main Authors: | , |
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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2604.04737 |
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| _version_ | 1866910108835905536 |
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| author | Gao, Yuchen Zhang, Qi |
| author_facet | Gao, Yuchen Zhang, Qi |
| contents | We aim to make learned point cloud compression deployable for low-latency streaming on mobile systems. While learned point cloud compression has shown strong coding efficiency, practical deployment on mobile platforms remains challenging because neural inference and entropy coding still incur substantial runtime overhead. This issue is critical for immersive 3D communication, where dense geometry must be delivered under tight end-to-end (E2E) latency and compute constraints. In this paper, we present LEAN-3D, a compute-aware point cloud codec for low-latency streaming. LEAN-3D designs a lightweight learned occupancy model at the shallow levels of a sparse occupancy hierarchy, where structural uncertainty is highest, and develops a lightweight deterministic coding scheme for the deep hierarchy tailored to the near-unary regime. We implement the complete encoder/decoder pipeline and evaluate it on an NVIDIA Jetson Orin Nano edge device and a desktop host. In addition, LEAN-3D addresses the decoding failures observed in cross-platform deployment of learned codecs. Such failures arise from numerical inconsistencies in lossless entropy decoding across heterogeneous platforms. Experiments show that LEAN-3D achieves 3-5x latency reduction across datasets, reduces total edge-side energy consumption by up to 5.1x, and delivers lower sustained E2E latency under bandwidth-limited streaming. These results bring learned point cloud compression closer to deployable mobile 3D streaming. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_04737 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LEAN-3D: Low-latency Hierarchical Point Cloud Codec for Mobile 3D Streaming Gao, Yuchen Zhang, Qi Signal Processing We aim to make learned point cloud compression deployable for low-latency streaming on mobile systems. While learned point cloud compression has shown strong coding efficiency, practical deployment on mobile platforms remains challenging because neural inference and entropy coding still incur substantial runtime overhead. This issue is critical for immersive 3D communication, where dense geometry must be delivered under tight end-to-end (E2E) latency and compute constraints. In this paper, we present LEAN-3D, a compute-aware point cloud codec for low-latency streaming. LEAN-3D designs a lightweight learned occupancy model at the shallow levels of a sparse occupancy hierarchy, where structural uncertainty is highest, and develops a lightweight deterministic coding scheme for the deep hierarchy tailored to the near-unary regime. We implement the complete encoder/decoder pipeline and evaluate it on an NVIDIA Jetson Orin Nano edge device and a desktop host. In addition, LEAN-3D addresses the decoding failures observed in cross-platform deployment of learned codecs. Such failures arise from numerical inconsistencies in lossless entropy decoding across heterogeneous platforms. Experiments show that LEAN-3D achieves 3-5x latency reduction across datasets, reduces total edge-side energy consumption by up to 5.1x, and delivers lower sustained E2E latency under bandwidth-limited streaming. These results bring learned point cloud compression closer to deployable mobile 3D streaming. |
| title | LEAN-3D: Low-latency Hierarchical Point Cloud Codec for Mobile 3D Streaming |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2604.04737 |