20 citations · 21 across the 4 of their papers we have counts for
6 papers
Correction Training for Diffusion Models
Donghoon Ahn, Runqian Wang, Alexei A. Efros +2
Iterative sampling in diffusion models can lead to error accumulation due to exposure bias. The denoiser is trained on real images interpolated with noise, but at inference its inp…
Repurposing Marigold for Zero-Shot Metric Depth Estimation via Defocus Blur Cues
Chinmay Talegaonkar, Nikhil Gandudi Suresh, Zachary Novack +3
Recent monocular metric depth estimation (MMDE) methods have made notable progress towards zero-shot generalization. However, they still exhibit a significant performance drop on o…
A Differentiable Wave Optics Model for End-to-End Computational Imaging System Optimization
Chi-Jui Ho, Yash Belhe, Steve Rotenberg +3
End-to-end optimization, which simultaneously optimizes optics and algorithms, has emerged as a powerful data-driven method for computational imaging system design. This method ach…
Volumetrically Consistent 3D Gaussian Rasterization
Chinmay Talegaonkar, Yash Belhe, Ravi Ramamoorthi +1
Recently, 3D Gaussian Splatting (3DGS) has enabled photorealistic view synthesis at high inference speeds. However, its splatting-based rendering model makes several approximations…
A Construct-Optimize Approach to Sparse View Synthesis without Camera Pose
Kaiwen Jiang, Yang Fu, Mukund Varma T +4
Novel view synthesis from a sparse set of input images is a challenging problem of great practical interest, especially when camera poses are absent or inaccurate. Direct optimizat…
Importance Sampling BRDF Derivatives
Yash Belhe, Bing Xu, Sai Praveen Bangaru +2
We propose a set of techniques to efficiently importance sample the derivatives of several BRDF models. In differentiable rendering, BRDFs are replaced by their differential BRDF c…