3 papers
cs.CV2025
Towards Learning to Complete Anything in Lidar
Ayca Takmaz, Cristiano Saltori, Neehar Peri +4
We propose CAL (Complete Anything in Lidar) for Lidar-based shape-completion in-the-wild. This is closely related to Lidar-based semantic/panoptic scene completion. However, contem…
cs.CV2023
An Empirical Analysis of Range for 3D Object Detection
Neehar Peri, Mengtian Li, Benjamin Wilson +3
LiDAR-based 3D detection plays a vital role in autonomous navigation. Surprisingly, although autonomous vehicles (AVs) must detect both near-field objects (for collision avoidance)…
cs.CV2023
ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active Learning
Wesley Chen, Andrew Edgley, Raunak Hota +5
In recent years, supervised learning has become the dominant paradigm for training deep-learning based methods for 3D object detection. Lately, the academic community has studied 3…