55 citations · 66 across the 5 of their papers we have counts for
6 papers
Robust Point Cloud Processing through Positional Embedding
Jianqiao Zheng, Xueqian Li, Sameera Ramasinghe +1
End-to-end trained per-point embeddings are an essential ingredient of any state-of-the-art 3D point cloud processing such as detection or alignment. Methods like PointNet, or the…
Fast Neural Scene Flow
Xueqian Li, Jianqiao Zheng, Francesco Ferroni +2
Neural Scene Flow Prior (NSFP) is of significant interest to the vision community due to its inherent robustness to out-of-distribution (OOD) effects and its ability to deal with d…
Trading Positional Complexity vs. Deepness in Coordinate Networks
Jianqiao Zheng, Sameera Ramasinghe, Xueqian Li +1
It is well noted that coordinate-based MLPs benefit -- in terms of preserving high-frequency information -- through the encoding of coordinate positions as an array of Fourier feat…
PointNetLK Revisited
Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey
We address the generalization ability of recent learning-based point cloud registration methods. Despite their success, these approaches tend to have poor performance when applied…
One Framework to Register Them All: PointNet Encoding for Point Cloud Alignment
Vinit Sarode, Xueqian Li, Hunter Goforth +5
PointNet has recently emerged as a popular representation for unstructured point cloud data, allowing application of deep learning to tasks such as object detection, segmentation a…
PCRNet: Point Cloud Registration Network using PointNet Encoding
Vinit Sarode, Xueqian Li, Hunter Goforth +4
PointNet has recently emerged as a popular representation for unstructured point cloud data, allowing application of deep learning to tasks such as object detection, segmentation a…