1 citations · 1 across the 5 of their papers we have counts for
5 papers
SharpSplat: Edge-Regularized 3D Gaussian Splatting for High Fidelity Urban Building Reconstruction from UAV images
Porus Vaid, Shivam Chopra, Vaibhav Kumar
Reconstructing high-fidelity 3D building models from UAV imagery is essential for large-scale digital twin development. However, existing 3D Gaussian Splatting (3DGS) techniques of…
Range-Edit: Semantic Mask Guided Outdoor LiDAR Scene Editing
Suchetan G. Uppur, Hemant Kumar, Vaibhav Kumar
Training autonomous driving and navigation systems requires large and diverse point cloud datasets that capture complex edge case scenarios from various dynamic urban settings. Acq…
RL-AD-Net: Reinforcement Learning Guided Adaptive Displacement in Latent Space for Refined Point Cloud Completion
Bhanu Pratap Paregi, Vaibhav Kumar
Recent point cloud completion models, including transformer-based, denoising-based, and other state-of-the-art approaches, generate globally plausible shapes from partial inputs bu…
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images
Pushpendra Dhakara, Prachi Chachodhia, Vaibhav Kumar
Unstructured urban environments present unique challenges for scene understanding and generalization due to their complex and diverse layouts. We introduce SynthGenNet, a self-supe…
Towards Explainable LiDAR Point Cloud Semantic Segmentation via Gradient Based Target Localization
Abhishek Kuriyal, Vaibhav Kumar
Semantic Segmentation (SS) of LiDAR point clouds is essential for many applications, such as urban planning and autonomous driving. While much progress has been made in interpretin…