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RBF Weighted Hyper-Involution for RGB-D Object Detection
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.
Dynamic Edge Weights in Graph Neural Networks for 3D Object Detection
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.
TCM-ICP: Transformation Compatibility Measure for Registering Multiple LIDAR Scans
In this work, we present an algorithm for registering multiple, overlapping LiDAR scans.
