1 code implementation • ICCV 2023 • Alexander C. Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown, Deepak Pathak
Our generative approach to classification, which we call Diffusion Classifier, attains strong results on a variety of benchmarks and outperforms alternative methods of extracting knowledge from diffusion models.
Ranked #1 on
Image Classification
on ObjectNet (ImageNet classes)
no code implementations • 12 Jun 2022 • Trevor Houchens, Cheng-You Lu, Shivam Duggal, Rao Fu, Srinath Sridhar
We propose Omnidirectional Distance Fields (ODFs), a new 3D shape representation that encodes geometry by storing the depth to the object's surface from any 3D position in any viewing direction.
1 code implementation • CVPR 2022 • Shivam Duggal, Deepak Pathak
The 3D shapes are generated implicitly as deformations to a category-specific signed distance field and are learned in an unsupervised manner solely from unaligned image collections and their poses without any 3D supervision.
no code implementations • 18 Jan 2021 • Shivam Duggal, ZiHao Wang, Wei-Chiu Ma, Sivabalan Manivasagam, Justin Liang, Shenlong Wang, Raquel Urtasun
Reconstructing high-quality 3D objects from sparse, partial observations from a single view is of crucial importance for various applications in computer vision, robotics, and graphics.
no code implementations • CVPR 2021 • Yun Chen, Frieda Rong, Shivam Duggal, Shenlong Wang, Xinchen Yan, Sivabalan Manivasagam, Shangjie Xue, Ersin Yumer, Raquel Urtasun
Scalable sensor simulation is an important yet challenging open problem for safety-critical domains such as self-driving.
1 code implementation • ICCV 2019 • Shivam Duggal, Shenlong Wang, Wei-Chiu Ma, Rui Hu, Raquel Urtasun
Our goal is to significantly speed up the runtime of current state-of-the-art stereo algorithms to enable real-time inference.