2 citations · 3 across the 10 of their papers we have counts for
10 papers
TensoIS: A Step Towards Feed-Forward Tensorial Inverse Subsurface Scattering for Perlin Distributed Heterogeneous Media
Ashish Tiwari, Satyam Bhardwaj, Yash Bachwana +4
Estimating scattering parameters of heterogeneous media from images is a severely under-constrained and challenging problem. Most of the existing approaches model BSSRDF either thr…
Hand Shadow Art: A Differentiable Rendering Perspective
Aalok Gangopadhyay, Prajwal Singh, Ashish Tiwari +1
Shadow art is an exciting form of sculptural art that produces captivating artistic effects through the 2D shadows cast by 3D shapes. Hand shadows, also known as shadow puppetry or…
RASP: Revisiting 3D Anamorphic Art for Shadow-Guided Packing of Irregular Objects
Soumyaratna Debnath, Ashish Tiwari, Kaustubh Sadekar +1
Recent advancements in learning-based methods have opened new avenues for exploring and interpreting art forms, such as shadow art, origami, and sketch art, through computational m…
ScribGen: Generating Scribble Art Through Metaheuristics
Soumyaratna Debnath, Ashish Tiwari, Shanmuganathan Raman
Art has long been a medium for individuals to engage with the world. Scribble art, a form of abstract visual expression, features spontaneous, gestural strokes made with pens or br…
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…
Revisiting Heterophily in Graph Convolution Networks by Learning Representations Across Topological and Feature Spaces
Ashish Tiwari, Sresth Tosniwal, Shanmuganathan Raman
Graph convolution networks (GCNs) have been enormously successful in learning representations over several graph-based machine learning tasks. Specific to learning rich node repres…
DeepPS2: Revisiting Photometric Stereo Using Two Differently Illuminated Images
Ashish Tiwari, Shanmuganathan Raman
Photometric stereo, a problem of recovering 3D surface normals using images of an object captured under different lightings, has been of great interest and importance in computer v…