108 citations · 172 across the 5 of their papers we have counts for
6 papers
WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environments
Kavisha Vidanapathirana, Joshua Knights, Stephen Hausler +8
Recent progress in semantic scene understanding has primarily been enabled by the availability of semantically annotated bi-modal (camera and LiDAR) datasets in urban environments.…
Multi-Body Neural Scene Flow
Kavisha Vidanapathirana, Shin-Fang Chng, Xueqian Li +1
The test-time optimization of scene flow - using a coordinate network as a neural prior - has gained popularity due to its simplicity, lack of dataset bias, and state-of-the-art pe…
Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments
Joshua Knights, Kavisha Vidanapathirana, Milad Ramezani +3
Many existing datasets for lidar place recognition are solely representative of structured urban environments, and have recently been saturated in performance by deep learning base…
Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization
Kavisha Vidanapathirana, Peyman Moghadam, Sridha Sridharan +1
In large-scale metric localization, an incorrect result during retrieval will lead to an incorrect pose estimate or loop closure. Re-ranking methods propose to take into account al…
LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition
Kavisha Vidanapathirana, Milad Ramezani, Peyman Moghadam +2
Retrieval-based place recognition is an efficient and effective solution for re-localization within a pre-built map, or global data association for Simultaneous Localization and Ma…
Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling
Kavisha Vidanapathirana, Peyman Moghadam, Ben Harwood +3
Place Recognition enables the estimation of a globally consistent map and trajectory by providing non-local constraints in Simultaneous Localisation and Mapping (SLAM). This paper…