14 citations · 68 across the 11 of their papers we have counts for
17 papers · 1 filter
CC-3DT: Panoramic 3D Object Tracking via Cross-Camera Fusion
Tobias Fischer, Yung-Hsu Yang, Suryansh Kumar +2
To track the 3D locations and trajectories of the other traffic participants at any given time, modern autonomous vehicles are equipped with multiple cameras that cover the vehicle…
Multi-View Photometric Stereo Revisited
Berk Kaya, Suryansh Kumar, Carlos Oliveira +2
Multi-view photometric stereo (MVPS) is a preferred method for detailed and precise 3D acquisition of an object from images. Although popular methods for MVPS can provide outstandi…
Robustifying the Multi-Scale Representation of Neural Radiance Fields
Nishant Jain, Suryansh Kumar, Luc Van Gool
Neural Radiance Fields (NeRF) recently emerged as a new paradigm for object representation from multi-view (MV) images. Yet, it cannot handle multi-scale (MS) images and camera pos…
Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fields
Soomin Lee, Le Chen, Jiahao Wang +3
In this paper, we tackle the problem of active robotic 3D reconstruction of an object. In particular, we study how a mobile robot with an arm-held camera can select a favorable num…
Uncertainty-Aware Deep Multi-View Photometric Stereo
Berk Kaya, Suryansh Kumar, Carlos Oliveira +2
This paper presents a simple and effective solution to the longstanding classical multi-view photometric stereo (MVPS) problem. It is well-known that photometric stereo (PS) is exc…
Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo
Francesco Sarno, Suryansh Kumar, Berk Kaya +3
We present an automated machine learning approach for uncalibrated photometric stereo (PS). Our work aims at discovering lightweight and computationally efficient PS neural network…