2 citations · 2 across the 19 of their papers we have counts for
5 papers · 2 filters
BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning
Prajwal Singh, Gautam Vashishtha, Indra Deep Mastan +1
The success of deep learning in supervised fine-grained recognition for domain-specific tasks relies heavily on expert annotations. The Open-Set for fine-grained Self-Supervised Le…
Incremental Multi-Scene Modeling via Continual Neural Graphics Primitives
Prajwal Singh, Ashish Tiwari, Gautam Vashishtha +1
Neural radiance fields (NeRF) have revolutionized photorealistic rendering of novel views for 3D scenes. Despite their growing popularity and efficiency as 3D resources, NeRFs face…
LIPIDS: Learning-based Illumination Planning In Discretized (Light) Space for Photometric Stereo
Ashish Tiwari, Mihir Sutariya, Shanmuganathan Raman
Photometric stereo is a powerful method for obtaining per-pixel surface normals from differently illuminated images of an object. While several methods address photometric stereo w…
MERLiN: Single-Shot Material Estimation and Relighting for Photometric Stereo
Ashish Tiwari, Satoshi Ikehata, Shanmuganathan Raman
Photometric stereo typically demands intricate data acquisition setups involving multiple light sources to recover surface normals accurately. In this paper, we propose MERLiN, an…
SS-SfP:Neural Inverse Rendering for Self Supervised Shape from (Mixed) Polarization
Ashish Tiwari, Shanmuganathan Raman
We present a novel inverse rendering-based framework to estimate the 3D shape (per-pixel surface normals and depth) of objects and scenes from single-view polarization images, the…