18 citations · 38 across the 7 of their papers we have counts for
8 papers
LiHi-GS: LiDAR-Supervised Gaussian Splatting for Highway Driving Scene Reconstruction
Pou-Chun Kung, Xianling Zhang, Katherine A. Skinner +1
Photorealistic 3D scene reconstruction plays an important role in autonomous driving, enabling the generation of novel data from existing datasets to simulate safety-critical scena…
LANe: Lighting-Aware Neural Fields for Compositional Scene Synthesis
Akshay Krishnan, Amit Raj, Xianling Zhang +5
Neural fields have recently enjoyed great success in representing and rendering 3D scenes. However, most state-of-the-art implicit representations model static or dynamic scenes as…
SIMBAR: Single Image-Based Scene Relighting For Effective Data Augmentation For Automated Driving Vision Tasks
Xianling Zhang, Nathan Tseng, Ameerah Syed +2
Real-world autonomous driving datasets comprise of images aggregated from different drives on the road. The ability to relight captured scenes to unseen lighting conditions, in a c…
Deflating Dataset Bias Using Synthetic Data Augmentation
Nikita Jaipuria, Xianling Zhang, Rohan Bhasin +5
Deep Learning has seen an unprecedented increase in vision applications since the publication of large-scale object recognition datasets and introduction of scalable compute hardwa…
On the Role of Receptive Field in Unsupervised Sim-to-Real Image Translation
Nikita Jaipuria, Shubh Gupta, Praveen Narayanan +1
Generative Adversarial Networks (GANs) are now widely used for photo-realistic image synthesis. In applications where a simulated image needs to be translated into a realistic imag…
Incremental Learning of Motion Primitives for Pedestrian Trajectory Prediction at Intersections
Golnaz Habibi, Nikita Japuria, Jonathan P. How
This paper presents a novel incremental learning algorithm for pedestrian motion prediction, with the ability to improve the learned model over time when data is incrementally avai…
Context-Aware Pedestrian Motion Prediction In Urban Intersections
Golnaz Habibi, Nikita Jaipuria, Jonathan P. How
This paper presents a novel context-based approach for pedestrian motion prediction in crowded, urban intersections, with the additional flexibility of prediction in similar, but n…
A Transferable Pedestrian Motion Prediction Model for Intersections with Different Geometries
Nikita Jaipuria, Golnaz Habibi, Jonathan P. How
This paper presents a novel framework for accurate pedestrian intent prediction at intersections. Given some prior knowledge of the curbside geometry, the presented framework can a…