1 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
A Spatiotemporal Correspondence Approach to Unsupervised LiDAR Segmentation with Traffic Applications
Xiao Li, Pan He, Aotian Wu +2
We address the problem of unsupervised semantic segmentation of outdoor LiDAR point clouds in diverse traffic scenarios. The key idea is to leverage the spatiotemporal nature of a…
An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation
Aotian Wu, Pan He, Xiao Li +3
Most existing perception systems rely on sensory data acquired from cameras, which perform poorly in low light and adverse weather conditions. To resolve this limitation, we have w…
Learning Canonical Embeddings for Unsupervised Shape Correspondence with Locally Linear Transformations
Pan He, Patrick Emami, Sanjay Ranka +1
We present a new approach to unsupervised shape correspondence learning between pairs of point clouds. We make the first attempt to adapt the classical locally linear embedding alg…
Towards Improving the Generation Quality of Autoregressive Slot VAEs
Patrick Emami, Pan He, Sanjay Ranka +1
Unconditional scene inference and generation are challenging to learn jointly with a single compositional model. Despite encouraging progress on models that extract object-centric…
Learning Scene Dynamics from Point Cloud Sequences
Pan He, Patrick Emami, Sanjay Ranka +1
Understanding 3D scenes is a critical prerequisite for autonomous agents. Recently, LiDAR and other sensors have made large amounts of data available in the form of temporal sequen…