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Found 10 papers, 2 papers with code
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.
Simultaneous Localisation and Mapping with Quadric Surfaces
Human-made environments contain a lot of structure, and we seek to take advantage of this by enabling the use of quadric surfaces as features in SLAM systems.
ILabel: Interactive Neural Scene Labelling
ILabel's underlying model is a multilayer perceptron (MLP) trained from scratch in real-time to learn a joint neural scene representation.
Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation
We present a coarse-to-fine discretisation method that enables the use of discrete reinforcement learning approaches in place of unstable and data-inefficient actor-critic methods in continuous robotics domains.
Ranked #7 on
Robot Manipulation
on RLBench
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.
DeepFactors: Real-Time Probabilistic Dense Monocular SLAM
The ability to estimate rich geometry and camera motion from monocular imagery is fundamental to future interactive robotics and augmented reality applications.
