{"ID":2889045,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.21499","arxiv_id":"2507.21499","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","abstract":"Rendering is critical in fields like 3D modeling, AR/VR, and autonomous driving, where high-quality, real-time output is essential. Point-based neural rendering (PBNR) offers a photorealistic and efficient alternative to conventional methods, yet it is still challenging to achieve real-time rendering on mobile platforms. We pinpoint two major bottlenecks in PBNR pipelines: LoD search and splatting. LoD search suffers from workload imbalance and irregular memory access, making it inefficient on off-the-shelf GPUs. Meanwhile, splatting introduces severe warp divergence across GPU threads due to its inherent sparsity. To tackle these challenges, we propose SLTarch, an algorithm-architecture co-designed framework. At its core, SLTarch introduces SLTree, a dedicated subtree-based data structure, and LTcore, a specialized hardware architecture tailored for efficient LoD search. Additionally, we co-design a divergence-free splatting algorithm with our simple yet principled hardware augmentation, SPcore, to existing PBNR accelerators. Compared to a mobile GPU, SLTarch achieves 3.9$\\times$ speedup and 98\\% energy savings with negligible architecture overhead. Compared to existing accelerator designs, SLTarch achieves 1.8$\\times$ speedup with 54\\% energy savings.","short_abstract":"Rendering is critical in fields like 3D modeling, AR/VR, and autonomous driving, where high-quality, real-time output is essential. Point-based neural rendering (PBNR) offers a photorealistic and efficient alternative to conventional methods, yet it is still challenging to achieve real-time rendering on mobile platform...","url_abs":"https://arxiv.org/abs/2507.21499","url_pdf":"https://arxiv.org/pdf/2507.21499v1","authors":"[\"Xingyang Li\",\"Jie Jiang\",\"Yu Feng\",\"Yiming Gan\",\"Jieru Zhao\",\"Zihan Liu\",\"Jingwen Leng\",\"Minyi Guo\"]","published":"2025-07-29T04:46:48Z","proceeding":"cs.AR","tasks":"[\"cs.AR\"]","methods":"[]","has_code":false}