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Found 16 papers, 7 papers with code
What's in the Black Box? The False Negative Mechanisms Inside Object Detectors
Using this framework, we investigate why Faster R-CNN and RetinaNet fail to detect objects in benchmark vision datasets and robotics datasets.
InCloud: Incremental Learning for Point Cloud Place Recognition
In this paper we address the problem of incremental learning for point cloud place recognition and introduce InCloud, a structure-aware distillation-based approach which preserves the higher-order structure of the network's embedding space.
Point Cloud Segmentation Using Sparse Temporal Local Attention
Point clouds are a key modality used for perception in autonomous vehicles, providing the means for a robust geometric understanding of the surrounding environment.
The CSIRO Crown-of-Thorn Starfish Detection Dataset
Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are underway in an attempt to manage COTS populations to ecologically sustainable levels.
Dense Uncertainty Estimation
Deep neural networks can be roughly divided into deterministic neural networks and stochastic neural networks. The former is usually trained to achieve a mapping from input space to output space via maximum likelihood estimation for the weights, which leads to deterministic predictions during testing.
LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition
Experiments on two large-scale public benchmarks (KITTI and MulRan) show that our method achieves mean $F1_{max}$ scores of $0. 939$ and $0. 968$ on KITTI and MulRan respectively, achieving state-of-the-art performance while operating in near real-time.
DeepSeagrass Dataset
We introduce a dataset of seagrass images collected by a biologist snorkelling in Moreton Bay, Queensland, Australia, as described in our publication: arXiv:2009. 09924.
Multi-species Seagrass Detection and Classification from Underwater Images
Underwater surveys conducted using divers or robots equipped with customized camera payloads can generate a large number of images.
Scalable learning for bridging the species gap in image-based plant phenotyping
This study is applicable to use of synthetic data for automating the measurement of phenotypic traits.
Temporally Coherent Embeddings for Self-Supervised Video Representation Learning
The proposed method exploits inherent structure of unlabeled video data to explicitly enforce temporal coherency in the embedding space, rather than indirectly learning it through ranking or predictive proxy tasks.
Ranked #29 on
Self-Supervised Action Recognition
on UCF101
Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach
The demand for multimodal sensing systems for robotics is growing due to the increase in robustness, reliability and accuracy offered by these systems.
Synthetic Human Model Dataset for Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction
We introduce a synthetic dataset for evaluating non-rigid 3D human reconstruction based on conventional RGB-D cameras.
Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction
We propose a puppet model-based tracking approach using skeleton prior, which provides a better initialization for tracking articulated movements.
Deep Leaf Segmentation Using Synthetic Data
Our proposed approach achieves 90% leaf segmentation score on the A1 test set outperforming the-state-of-the-art approaches for the CVPPP Leaf Segmentation Challenge (LSC).
Non-rigid Reconstruction with a Single Moving RGB-D Camera
Our approach uses camera pose estimated from the rigid background for foreground tracking.
Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM
The concept of continuous-time trajectory representation has brought increased accuracy and efficiency to multi-modal sensor fusion in modern SLAM.
Robotics
