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Found 27 papers, 5 papers with code
BodySLAM: Joint Camera Localisation, Mapping, and Human Motion Tracking
Through a series of experiments on video sequences of human motion captured by a moving monocular camera, we demonstrate that BodySLAM improves estimates of all human body parameters and camera poses when compared to estimating these separately.
OKVIS2: Realtime Scalable Visual-Inertial SLAM with Loop Closure
Robust and accurate state estimation remains a challenge in robotics, Augmented, and Virtual Reality (AR/VR), even as Visual-Inertial Simultaneous Localisation and Mapping (VI-SLAM) getting commoditised.
SIMstack: A Generative Shape and Instance Model for Unordered Object Stacks
At test time, our model can generate 3D shape and instance segmentation from a single depth view, probabilistically sampling proposals for the occluded region from the learned latent space.
In-Place Scene Labelling and Understanding with Implicit Scene Representation
Semantic labelling is highly correlated with geometry and radiance reconstruction, as scene entities with similar shape and appearance are more likely to come from similar classes.
Deep Probabilistic Feature-metric Tracking
Dense image alignment from RGB-D images remains a critical issue for real-world applications, especially under challenging lighting conditions and in a wide baseline setting.
Bundle Adjustment on a Graph Processor
Graph processors such as Graphcore's Intelligence Processing Unit (IPU) are part of the major new wave of novel computer architecture for AI, and have a general design with massively parallel computation, distributed on-chip memory and very high inter-core communication bandwidth which allows breakthrough performance for message passing algorithms on arbitrary graphs.
Comparing View-Based and Map-Based Semantic Labelling in Real-Time SLAM
Generally capable Spatial AI systems must build persistent scene representations where geometric models are combined with meaningful semantic labels.
Towards Bounding-Box Free Panoptic Segmentation
Our approach is distinct from previous works in panoptic segmentation that rely on a combination of a semantic segmentation network with a computationally costly instance segmentation network based on bounding box proposals, such as Mask R-CNN, to guide the prediction of instance labels using a Mixture-of-Expert (MoE) approach.
Event-based Vision: A Survey
Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur.
SceneCode: Monocular Dense Semantic Reconstruction using Learned Encoded Scene Representations
Systems which incrementally create 3D semantic maps from image sequences must store and update representations of both geometry and semantic entities.
X-Section: Cross-Section Prediction for Enhanced RGBD Fusion
Detailed 3D reconstruction is an important challenge with application to robotics, augmented and virtual reality, which has seen impressive progress throughout the past years.
MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM
It can provide robust camera tracking in dynamic environments and at the same time, continuously estimate geometric, semantic, and motion properties for arbitrary objects in the scene.
LS-Net: Learning to Solve Nonlinear Least Squares for Monocular Stereo
In this paper, we propose LS-Net, a neural nonlinear least squares optimization algorithm which learns to effectively optimize these cost functions even in the presence of adversities.
InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset
Datasets have gained an enormous amount of popularity in the computer vision community, from training and evaluation of Deep Learning-based methods to benchmarking Simultaneous Localization and Mapping (SLAM).
Learning to Solve Nonlinear Least Squares for Monocular Stereo
In this paper, we propose a neural nonlinear least squares optimization algorithm which learns to effectively optimize these cost functions even in the presence of adversities.
Fusion++: Volumetric Object-Level SLAM
Reconstructed objects are stored in an optimisable 6DoF pose graph which is our only persistent map representation.
Towards an Embodied Semantic Fovea: Semantic 3D scene reconstruction from ego-centric eye-tracker videos
Incorporating the physical environment is essential for a complete understanding of human behavior in unconstrained every-day tasks.
CodeSLAM â Learning a Compact, Optimisable Representation for Dense Visual SLAM
Our approach is suitable for use in a keyframe-based monocular dense SLAM system: While each keyframe with a code can produce a depth map, the code can be optimised efficiently jointly with pose variables and together with the codes of overlapping keyframes to attain global consistency.
CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM
Our approach is suitable for use in a keyframe-based monocular dense SLAM system: While each keyframe with a code can produce a depth map, the code can be optimised efficiently jointly with pose variables and together with the codes of overlapping keyframes to attain global consistency.
SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-Training on Indoor Segmentation?
We compare the semantic segmentation performance of network weights produced from pre-training on RGB images from our dataset against generic VGG-16 ImageNet weights.
Semantic Texture for Robust Dense Tracking
We argue that robust dense SLAM systems can make valuable use of the layers of features coming from a standard CNN as a pyramid of `semantic texture' which is suitable for dense alignment while being much more robust to nuisance factors such as lighting than raw RGB values.
SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth
We introduce SceneNet RGB-D, expanding the previous work of SceneNet to enable large scale photorealistic rendering of indoor scene trajectories.
SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks
This not only produces a useful semantic 3D map, but we also show on the NYUv2 dataset that fusing multiple predictions leads to an improvement even in the 2D semantic labelling over baseline single frame predictions.
Deep Learning a Grasp Function for Grasping under Gripper Pose Uncertainty
With this, it is possible to achieve grasping robust to the gripper's pose uncertainty, by smoothing the grasp function with the pose uncertainty function.
Simultaneous Optical Flow and Intensity Estimation From an Event Camera
In a series of examples, we demonstrate the successful operation of our framework, including in situations where conventional cameras heavily suffer from dynamic range limitations or motion blur.
Pairwise Decomposition of Image Sequences for Active Multi-View Recognition
A multi-view image sequence provides a much richer capacity for object recognition than from a single image.
Place Recognition with Event-based Cameras and a Neural Implementation of SeqSLAM
Event-based cameras offer much potential to the fields of robotics and computer vision, in part due to their large dynamic range and extremely high "frame rates".
