no code implementations • 30 Sep 2023 • Mehfuz A Rahman, Jiju Peethambaran, Neil London
The proposed model consists of two new components: a depth guided hyper-involution that adapts dynamically based on the spatial interaction pattern in the raw depth map and an up-sampling based trainable fusion layer that combines the extracted depth and color image features without blocking the information transfer between them.
no code implementations • 17 Sep 2020 • Sumesh Thakur, Jiju Peethambaran
In each layer of the GNN, apart from the linear transformation which maps the per node input features to the corresponding higher level features, a per node masked attention by specifying different weights to different nodes in its first ring neighborhood is also performed.
no code implementations • 4 Jan 2020 • Aby Thomas, Adarsh Sunilkumar, Shankar Shylesh, Aby Abahai T., Subhasree Methirumangalath, Dong Chen, Jiju Peethambaran
In this work, we present an algorithm for registering multiple, overlapping LiDAR scans.