no code implementations • 1 Mar 2024 • Chunlin Li, Ruofan Liang, Hanrui Fan, Zhengen Zhang, Sankeerth Durvasula, Nandita Vijaykumar
We present a framework, DISORF, to enable online 3D reconstruction and visualization of scenes captured by resource-constrained mobile robots and edge devices.
1 code implementation • 15 Feb 2024 • Chen Yang, Sikuang Li, Jiemin Fang, Ruofan Liang, Lingxi Xie, Xiaopeng Zhang, Wei Shen, Qi Tian
Then we construct a Gaussian repair model based on diffusion models to supplement the omitted object information, where Gaussians are further refined.
no code implementations • 1 Dec 2023 • Sankeerth Durvasula, Adrian Zhao, Fan Chen, Ruofan Liang, Pawan Kumar Sanjaya, Nandita Vijaykumar
In this work, we observe that the gradient computation phase during training is a significant bottleneck on GPUs due to the large number of atomic operations that need to be processed.
1 code implementation • ICCV 2023 • Ruofan Liang, Huiting Chen, Chunlin Li, Fan Chen, Selvakumar Panneer, Nandita Vijaykumar
In this work, we introduce ENVIDR, a rendering and modeling framework for high-quality rendering and reconstruction of surfaces with challenging specular reflections.
no code implementations • 30 Jan 2023 • Daniel Snider, Ruofan Liang
Machine learning (ML) compilers are an active area of research because they offer the potential to automatically speedup tensor programs.
no code implementations • 15 Oct 2022 • Ruofan Liang, Jiahao Zhang, Haoda Li, Chen Yang, Yushi Guan, Nandita Vijaykumar
To enable more accurate illumination updates after deformation, we use the shadow mapping technique to approximate the light visibility updates caused by geometry editing.
no code implementations • ICLR 2022 • Ruofan Liang, Hongyi Sun, Nandita Vijaykumar
In this work, we aim to accelerate inference and training of coordinate-based MLPs for implicit neural representations by proposing a new split MLP architecture, CoordX.
no code implementations • 10 Jan 2022 • Ruofan Liang, Bingsheng He, Shengen Yan, Peng Sun
Multi-tenant machine learning services have become emerging data-intensive workloads in data centers with heavy usage of GPU resources.
no code implementations • ICLR 2020 • Ruofan Liang, Tianlin Li, Longfei Li, Jing Wang, Quanshi Zhang
As a generic tool, our method can be broadly used for different applications.