1 code implementation • CVPR 2021 • Kun Qian, Shilin Zhu, Xinyu Zhang, Li Erran Li
Vehicle detection with visual sensors like lidar and camera is one of the critical functions enabling autonomous driving.
no code implementations • 5 Oct 2020 • Shilin Zhu, Zexiang Xu, Tiancheng Sun, Alexandr Kuznetsov, Mark Meyer, Henrik Wann Jensen, Hao Su, Ravi Ramamoorthi
To fully make use of our deep neural network, we partition the scene space into an adaptive hierarchical grid, in which we apply our network to reconstruct high-quality sampling distributions for any local region in the scene.
no code implementations • 26 May 2020 • Shilin Zhu
In the past few years, machine learning-based approaches have had some great success for rendering animated feature films.
no code implementations • 25 Apr 2020 • Shilin Zhu, Zexiang Xu, Henrik Wann Jensen, Hao Su, Ravi Ramamoorthi
This network is easy to incorporate in many previous photon mapping methods (by simply swapping the kernel density estimator) and can produce high-quality reconstructions of complex global illumination effects like caustics with an order of magnitude fewer photons compared to previous photon mapping methods.
1 code implementation • CVPR 2020 • Shuo Cheng, Zexiang Xu, Shilin Zhu, Zhuwen Li, Li Erran Li, Ravi Ramamoorthi, Hao Su
In contrast, we propose adaptive thin volumes (ATVs); in an ATV, the depth hypothesis of each plane is spatially varying, which adapts to the uncertainties of previous per-pixel depth predictions.
Ranked #13 on 3D Reconstruction on DTU
5 code implementations • CVPR 2019 • Kaichun Mo, Shilin Zhu, Angel X. Chang, Li Yi, Subarna Tripathi, Leonidas J. Guibas, Hao Su
We present PartNet: a consistent, large-scale dataset of 3D objects annotated with fine-grained, instance-level, and hierarchical 3D part information.
Ranked #3 on 3D Instance Segmentation on PartNet
no code implementations • 4 Oct 2018 • Cheng Fu, Shilin Zhu, Hao Su, Ching-En Lee, Jishen Zhao
Thus there does exist redundancy that can be exploited to further reduce the amount of on-chip computations.
1 code implementation • CVPR 2019 • Shilin Zhu, Xin Dong, Hao Su
Binary neural networks (BNN) have been studied extensively since they run dramatically faster at lower memory and power consumption than floating-point networks, thanks to the efficiency of bit operations.