To close this gap, we introduce LaMAR, a new benchmark with a comprehensive capture and GT pipeline that co-registers realistic trajectories and sensor streams captured by heterogeneous AR devices in large, unconstrained scenes.
Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics modeling.
3D scene graphs (3DSGs) are an emerging description; unifying symbolic, topological, and metric scene representations.
Our key idea is to implicitly encode the appearance of a sparse yet salient set of 3D scene points into a convolutional neural network (CNN) that can detect these scene points in query images whenever they are visible.
Visual localization and mapping is the key technology underlying the majority of mixed reality and robotics systems.
Natural human communication is nuanced and inherently multi-modal.
However, the latent space learned by such approaches is typically not interpretable, resulting in less flexibility.
For many applications in low-power real-time robotics, stereo cameras are the sensors of choice for depth perception as they are typically cheaper and more versatile than their active counterparts.
Deep Neural Networks (DNNs) have demonstrated exceptional performance on most recognition tasks such as image classification and segmentation.
Rotoscoping, the detailed delineation of scene elements through a video shot, is a painstaking task of tremendous importance in professional post-production pipelines.
A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions.
It is not always possible to recognise objects and infer material properties for a scene from visual cues alone, since objects can look visually similar whilst being made of very different materials.
Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes.
Ranked #18 on Visual Object Tracking on TrackingNet