activity
20232025
most citedNeuFlow v2: Push High-Efficiency Optical Flow To the Limit

2 citations · 5 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2025

SNAP: Towards Segmenting Anything in Any Point Cloud

Aniket Gupta, Hanhui Wang, Charles Saunders +3

Interactive 3D point cloud segmentation enables efficient annotation of complex 3D scenes through user-guided prompts. However, current approaches are typically restricted in scope…

cs.CV2024★ 2 cited

NeuFlow v2: Push High-Efficiency Optical Flow To the Limit

Zhiyong Zhang, Aniket Gupta, Huaizu Jiang +1

Real-time high-accuracy optical flow estimation is critical for a variety of real-world robotic applications. However, current learning-based methods often struggle to balance accu…

cs.CV2024★ 1 cited

Towards Long Term SLAM on Thermal Imagery

Colin Keil, Aniket Gupta, Pushyami Kaveti +1

Visual SLAM with thermal imagery, and other low contrast visually degraded environments such as underwater, or in areas dominated by snow and ice, remain a difficult problem for ma…

cs.CV2023

A Strong Baseline for Point Cloud Registration via Direct Superpoints Matching

Aniket Gupta, Yiming Xie, Hanumant Singh +1

Deep neural networks endow the downsampled superpoints with highly discriminative feature representations. Previous dominant point cloud registration approaches match these feature…

cs.RO2023

Challenges of Indoor SLAM: A multi-modal multi-floor dataset for SLAM evaluation

Pushyami Kaveti, Aniket Gupta, Dennis Giaya +5

Robustness in Simultaneous Localization and Mapping (SLAM) remains one of the key challenges for the real-world deployment of autonomous systems. SLAM research has seen significant…

cs.RO2023★ 1 cited

Bridging the Domain Gap between Synthetic and Real-World Data for Autonomous Driving

Xiangyu Bai, Yedi Luo, Le Jiang +4

Modern autonomous systems require extensive testing to ensure reliability and build trust in ground vehicles. However, testing these systems in the real-world is challenging due to…

cs.RO2023★ 1 cited

Temporal-controlled Frame Swap for Generating High-Fidelity Stereo Driving Data for Autonomy Analysis

Yedi Luo, Xiangyu Bai, Le Jiang +4

This paper presents a novel approach, TeFS (Temporal-controlled Frame Swap), to generate synthetic stereo driving data for visual simultaneous localization and mapping (vSLAM) task…

cs.CV2023

Bayesian Decision Making to Localize Visual Queries in 2D

Syed Asjad, Aniket Gupta, Hanumant Singh

This report describes our approach for the EGO4D 2023 Visual Query 2D Localization Challenge. Our method aims to reduce the number of False Positives (FP) that occur because of hig…