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Prashant Kumar

4 accepted papers

2025

Revisiting Point Cloud Completion: Are We Ready For The Real-World?

ICCV 2025poster

Point clouds acquired in constrained, challenging, uncontrolled, and multi-sensor real-world settings are noisy, incomplete, and non-uniformly sparse. This presents acute challenges for the vital task of point cloud completion. Using tools from Algebraic Topology and Persistent Homology (PH), we dem…

2024

GLiDR: Topologically Regularized Graph Generative Network for Sparse LiDAR Point Clouds

CVPR 2024poster

Sparse LiDAR point clouds cause severe loss of detail of static structures and reduce the density of static points available for navigation. Reduced density can be detrimental to navigation under several scenarios. We observe that despite high sparsity in most cases the global topology of LiDAR outl…

2021

Dynamic to Static Lidar Scan Reconstruction Using Adversarially Trained Auto Encoder

AAAI 2021technical

Accurate reconstruction of static environments from LiDAR scans of scenes containing dynamic objects, which we refer to as Dynamic to Static Translation (DST), is an important area of research in Autonomous Navigation. This problem has been recently explored for visual SLAM, but to the best of our k…

Cited by 8SourcePDFScholar