7 citations · 15 across the 6 of their papers we have counts for
6 papers
Your Diffusion Model is Secretly a Zero-Shot Classifier
Alexander C. Li, Mihir Prabhudesai, Shivam Duggal +2
The recent wave of large-scale text-to-image diffusion models has dramatically increased our text-based image generation abilities. These models can generate realistic images for a…
NeuralODF: Learning Omnidirectional Distance Fields for 3D Shape Representation
Trevor Houchens, Cheng-You Lu, Shivam Duggal +2
In visual computing, 3D geometry is represented in many different forms including meshes, point clouds, voxel grids, level sets, and depth images. Each representation is suited for…
Topologically-Aware Deformation Fields for Single-View 3D Reconstruction
Shivam Duggal, Deepak Pathak
We present a framework for learning 3D object shapes and dense cross-object 3D correspondences from just an unaligned category-specific image collection. The 3D shapes are generate…
Mending Neural Implicit Modeling for 3D Vehicle Reconstruction in the Wild
Shivam Duggal, Zihao Wang, Wei-Chiu Ma +4
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 grap…
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving
Yun Chen, Frieda Rong, Shivam Duggal +6
Scalable sensor simulation is an important yet challenging open problem for safety-critical domains such as self-driving. Current works in image simulation either fail to be photor…
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch
Shivam Duggal, Shenlong Wang, Wei-Chiu Ma +2
Our goal is to significantly speed up the runtime of current state-of-the-art stereo algorithms to enable real-time inference. Towards this goal, we developed a differentiable Patc…