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Found 46 papers, 10 papers with code
DynamicGlue: Epipolar and Time-Informed Data Association in Dynamic Environments using Graph Neural Networks
The assumption of a static environment is common in many geometric computer vision tasks like SLAM but limits their applicability in highly dynamic scenes.
FuncGrasp: Learning Object-Centric Neural Grasp Functions from Single Annotated Example Object
We present FuncGrasp, a framework that can infer dense yet reliable grasp configurations for unseen objects using one annotated object and single-view RGB-D observation via categorical priors.
NeRF-VO: Real-Time Sparse Visual Odometry with Neural Radiance Fields
We introduce a novel monocular visual odometry (VO) system, NeRF-VO, that integrates learning-based sparse visual odometry for low-latency camera tracking and a neural radiance scene representation for sophisticated dense reconstruction and novel view synthesis.
Dynamic LiDAR Re-simulation using Compositional Neural Fields
We introduce DyNFL, a novel neural field-based approach for high-fidelity re-simulation of LiDAR scans in dynamic driving scenes.
DiffCAD: Weakly-Supervised Probabilistic CAD Model Retrieval and Alignment from an RGB Image
We formulate this as a conditional generative task, leveraging diffusion to learn implicit probabilistic models capturing the shape, pose, and scale of CAD objects in an image.
Anthropomorphic Grasping with Neural Object Shape Completion
The progressive prevalence of robots in human-suited environments has given rise to a myriad of object manipulation techniques, in which dexterity plays a paramount role.
AP$n$P: A Less-constrained P$n$P Solver for Pose Estimation with Unknown Anisotropic Scaling or Focal Lengths
Experimental results on both simulated and real datasets demonstrate the effectiveness of AP$n$P as a more flexible and practical solution to camera pose estimation.
Accurate and Interactive Visual-Inertial Sensor Calibration with Next-Best-View and Next-Best-Trajectory Suggestion
Visual-Inertial (VI) sensors are popular in robotics, self-driving vehicles, and augmented and virtual reality applications.
GloPro: Globally-Consistent Uncertainty-Aware 3D Human Pose Estimation & Tracking in the Wild
An accurate and uncertainty-aware 3D human body pose estimation is key to enabling truly safe but efficient human-robot interactions.
BodySLAM++: Fast and Tightly-Coupled Visual-Inertial Camera and Human Motion Tracking
Robust, fast, and accurate human state - 6D pose and posture - estimation remains a challenging problem.
Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning
We show that intentions of human players, i. e. the precursor of goal-oriented decisions, can be robustly predicted from eye gaze even for the long-horizon sparse rewards task of Montezuma's Revenge - one of the most challenging RL tasks in the Atari2600 game suite.
SimpleMapping: Real-Time Visual-Inertial Dense Mapping with Deep Multi-View Stereo
The sparse depth from VIO is firstly completed by a single-view depth completion network.
Incremental Dense Reconstruction from Monocular Video with Guided Sparse Feature Volume Fusion
Incrementally recovering 3D dense structures from monocular videos is of paramount importance since it enables various robotics and AR applications.
Event-based Non-Rigid Reconstruction from Contours
Visual reconstruction of fast non-rigid object deformations over time is a challenge for conventional frame-based cameras.
Learning to Complete Object Shapes for Object-level Mapping in Dynamic Scenes
In this paper, we propose a novel object-level mapping system that can simultaneously segment, track, and reconstruct objects in dynamic scenes.
Visual-Inertial Multi-Instance Dynamic SLAM with Object-level Relocalisation
In this paper, we present a tightly-coupled visual-inertial object-level multi-instance dynamic SLAM system.
Towards the Probabilistic Fusion of Learned Priors into Standard Pipelines for 3D Reconstruction
While systems that pass the output of traditional multi-view stereo approaches to a network for regularisation or refinement currently seem to get the best results, it may be preferable to treat deep neural networks as separate components whose results can be probabilistically fused into geometry-based systems.
DeepFusion: Real-Time Dense 3D Reconstruction for Monocular SLAM using Single-View Depth and Gradient Predictions
While the keypoint-based maps created by sparse monocular simultaneous localisation and mapping (SLAM) systems are useful for camera tracking, dense 3D reconstructions may be desired for many robotic tasks.
Dense RGB-D-Inertial SLAM with Map Deformations
While dense visual SLAM methods are capable of estimating dense reconstructions of the environment, they suffer from a lack of robustness in their tracking step, especially when the optimisation is poorly initialised.
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".
