Search Results for author:
Found 15 papers, 8 papers with code
DatasetEquity: Are All Samples Created Equal? In The Quest For Equity Within Datasets
Data imbalance is a well-known issue in the field of machine learning, attributable to the cost of data collection, the difficulty of labeling, and the geographical distribution of the data.
Ref-DVGO: Reflection-Aware Direct Voxel Grid Optimization for an Improved Quality-Efficiency Trade-Off in Reflective Scene Reconstruction
To this end, we investigate an implicit-explicit approach based on conventional volume rendering to enhance the reconstruction quality and accelerate the training and rendering processes.
Locking On: Leveraging Dynamic Vehicle-Imposed Motion Constraints to Improve Visual Localization
In this research, we propose a middle ground, demonstrated in the context of autonomous vehicles, using dynamic vehicles to provide limited pose constraint information in a 6-DoF frame-by-frame PnP-RANSAC localization pipeline.
DisPlacing Objects: Improving Dynamic Vehicle Detection via Visual Place Recognition under Adverse Conditions
In this work we investigate whether a prior map can be leveraged to aid in the detection of dynamic objects in a scene without the need for a 3D map or pixel-level map-query correspondences.
Category-Level Pose Retrieval with Contrastive Features Learnt with Occlusion Augmentation
In both cases, the idea is to directly predict the pose of an object.
Improving Worst Case Visual Localization Coverage via Place-specific Sub-selection in Multi-camera Systems
6-DoF visual localization systems utilize principled approaches rooted in 3D geometry to perform accurate camera pose estimation of images to a map.
QAGAN: Adversarial Approach To Learning Domain Invariant Language Features
Training models that are robust to data domain shift has gained an increasing interest both in academia and industry.
Propagating State Uncertainty Through Trajectory Forecasting
As a result, perceptual uncertainties are not propagated through forecasting and predictions are frequently overconfident.
VR3Dense: Voxel Representation Learning for 3D Object Detection and Monocular Dense Depth Reconstruction
3D object detection and dense depth estimation are one of the most vital tasks in autonomous driving.
Meta-Regularization by Enforcing Mutual-Exclusiveness
Second, they also need to adapt to new novel unseen tasks at meta-test time again by using only a small amount of training data from that task.
S-BEV: Semantic Birds-Eye View Representation for Weather and Lighting Invariant 3-DoF Localization
We describe a light-weight, weather and lighting invariant, Semantic Bird's Eye View (S-BEV) signature for vision-based vehicle re-localization.
An A* Curriculum Approach to Reinforcement Learning for RGBD Indoor Robot Navigation
Training robots to navigate diverse environments is a challenging problem as it involves the confluence of several different perception tasks such as mapping and localization, followed by optimal path-planning and control.
Sim2Real for Self-Supervised Monocular Depth and Segmentation
Image-based learning methods for autonomous vehicle perception tasks require large quantities of labelled, real data in order to properly train without overfitting, which can often be incredibly costly.
CubifAE-3D: Monocular Camera Space Cubification for Auto-Encoder based 3D Object Detection
We introduce a method for 3D object detection using a single monocular image.
3D Object Detection From Monocular Images
Autonomous Vehicles
+3
Deflating Dataset Bias Using Synthetic Data Augmentation
Deep Learning has seen an unprecedented increase in vision applications since the publication of large-scale object recognition datasets and introduction of scalable compute hardware.
