Search Results for author:
Found 17 papers, 8 papers with code
Robustness Analysis on Foundational Segmentation Models
Due to the increase in computational resources and accessibility of data, an increase in large, deep learning models trained on copious amounts of data using self-supervised or semi-supervised learning have emerged.
SGAligner : 3D Scene Alignment with Scene Graphs
We propose SGAligner, the first method for aligning pairs of 3D scene graphs that is robust to in-the-wild scenarios (ie, unknown overlap -- if any -- and changes in the environment).
Ranked #1 on
Point Cloud Registration
on 3RScan
SGAligner: 3D Scene Alignment with Scene Graphs
We propose SGAligner, the first method for aligning pairs of 3D scene graphs that is robust to in-the-wild scenarios (i. e., unknown overlap - if any - and changes in the environment).
LaMAR: Benchmarking Localization and Mapping for Augmented Reality
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.
Learning to Simulate Realistic LiDARs
Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics modeling.
TASKOGRAPHY: Evaluating robot task planning over large 3D scene graphs
3D scene graphs (3DSGs) are an emerging description; unifying symbolic, topological, and metric scene representations.
Learning To Detect Scene Landmarks for Camera Localization
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.
Cross-Descriptor Visual Localization and Mapping
Visual localization and mapping is the key technology underlying the majority of mixed reality and robotics systems.
Live Reconstruction of Large-Scale Dynamic Outdoor Worlds
For each time step, our dynamic map maintains a relative pose of each volume with respect to the stationary background.
Multi-Modal Sequence Fusion via Recursive Attention for Emotion Recognition
Natural human communication is nuanced and inherently multi-modal.
DGPose: Deep Generative Models for Human Body Analysis
However, the latent space learned by such approaches is typically not interpretable, resulting in less flexibility.
Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded Devices
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.
On the Robustness of Semantic Segmentation Models to Adversarial Attacks
Deep Neural Networks (DNNs) have demonstrated exceptional performance on most recognition tasks such as image classification and segmentation.
ROAM: a Rich Object Appearance Model with Application to Rotoscoping
Rotoscoping, the detailed delineation of scene elements through a video shot, is a painstaking task of tremendous importance in professional post-production pipelines.
Playing Doom with SLAM-Augmented Deep Reinforcement Learning
A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions.
Joint Object-Material Category Segmentation from Audio-Visual Cues
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.
Staple: Complementary Learners for Real-Time Tracking
Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes.
Ranked #27 on
Visual Object Tracking
on TrackingNet
