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Found 13 papers, 6 papers with code
Self-improving object detection via disagreement reconciliation
Object detectors often experience a drop in performance when new environmental conditions are insufficiently represented in the training data.
Look around and learn: self-improving object detection by exploration
Object detectors often experience a drop in performance when new environmental conditions are insufficiently represented in the training data.
Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling
We study the problem of efficient semantic segmentation of large-scale 3D point clouds.
Learning Selective Sensor Fusion for States Estimation
By integrating the observations from different sensors, these mobile agents are able to perceive the environment and estimate system states, e. g. locations and orientations.
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
We study the problem of efficient semantic segmentation for large-scale 3D point clouds.
Ranked #3 on
Semantic Segmentation
on Toronto-3D L002
See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar
This paper presents the design, implementation and evaluation of milliMap, a single-chip millimetre wave (mmWave) radar based indoor mapping system targetted towards low-visibility environments to assist in emergency response.
DeepTIO: A Deep Thermal-Inertial Odometry with Visual Hallucination
The hallucination network is taught to predict fake visual features from thermal images by using Huber loss.
Selective Sensor Fusion for Neural Visual-Inertial Odometry
Deep learning approaches for Visual-Inertial Odometry (VIO) have proven successful, but they rarely focus on incorporating robust fusion strategies for dealing with imperfect input sensory data.
Neural Allocentric Intuitive Physics Prediction from Real Videos
In this framework, real images are first converted to a synthetic domain representation that reduces complexity arising from lighting and texture.
3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations
This is further confounded by the fact that shape information about encountered objects in the real world is often impaired by occlusions, noise and missing regions e. g. a robot manipulating an object will only be able to observe a partial view of the entire solid.
Defo-Net: Learning Body Deformation using Generative Adversarial Networks
Modelling the physical properties of everyday objects is a fundamental prerequisite for autonomous robots.
Robotics
Dense 3D Object Reconstruction from a Single Depth View
Unlike existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid with a high resolution of 256^3 by recovering the occluded/missing regions.
Fast Graph-Based Object Segmentation for RGB-D Images
We propose a modified Canny edge detector for extracting robust edges by using depth information and two simple cost functions for combining color and depth cues.
