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Found 11 papers, 4 papers with code
PanopticNDT: Efficient and Robust Panoptic Mapping
As the application scenarios of mobile robots are getting more complex and challenging, scene understanding becomes increasingly crucial.
Ranked #1 on
3D Semantic Segmentation
on Hypersim
Fusing Hand and Body Skeletons for Human Action Recognition in Assembly
As collaborative robots (cobots) continue to gain popularity in industrial manufacturing, effective human-robot collaboration becomes crucial.
How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks
Our research sheds light on the benefits of combining skeleton joints with object information for human action recognition in assembly tasks.
Efficient Multi-Task Scene Analysis with RGB-D Transformers
However, we show that the dual CNN-based encoder of EMSANet can be replaced with a single Transformer-based encoder.
Ranked #5 on
Semantic Segmentation
on ScanNetV2
ATTACH Dataset: Annotated Two-Handed Assembly Actions for Human Action Understanding
With the emergence of collaborative robots (cobots), human-robot collaboration in industrial manufacturing is coming into focus.
A Little Bit Attention Is All You Need for Person Re-Identification
Person re-identification plays a key role in applications where a mobile robot needs to track its users over a long period of time, even if they are partially unobserved for some time, in order to follow them or be available on demand.
Relate to Predict: Towards Task-Independent Knowledge Representations for Reinforcement Learning
For this, we introduce a semantic module that predicts an objects' semantic state based on its context.
Few-Shot Object Detection: A Comprehensive Survey
To avoid the need to acquire and annotate these huge amounts of data, few-shot object detection aims to learn from few object instances of new categories in the target domain.
Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis
In this paper, we propose an efficient and robust RGB-D segmentation approach that can be optimized to a high degree using NVIDIA TensorRT and, thus, is well suited as a common initial processing step in a complex system for scene analysis on mobile robots.
Ranked #2 on
Semantic Segmentation
on THUD Robotic Dataset
StickyPillars: Robust and Efficient Feature Matching on Point Clouds using Graph Neural Networks
Furthermore, we integrate our matching system into a LiDAR odometry pipeline yielding most accurate results on the KITTI odometry dataset.
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only.
