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Unsupervised learning of threshold for geometric verification in visual-based loop-closure
(2014)2014 IEEE International Conference on Robotics and Automation (ICRA)A potential loop-closure image pair passes the geometric verification test if the number of inliers from the computation of the geometric constraint with RANSAC exceed a pre-defined threshold. The choice of the threshold is critical to the success of identifying the correct loop-closure image pairs. However, the value for this threshold often varies for different datasets and is chosen empirically. In this paper, we propose an unsupervised ...Conference Paper -
3D Occlusion Inference from Silhouette Cues
(2007)2007 IEEE Conference on Computer Vision and Pattern RecognitionWe consider the problem of detecting and accounting for the presence of occluders in a 3D scene based on silhouette cues in video streams obtained from multiple, calibrated views. While well studied and robust in controlled environments, silhouette-based reconstruction of dynamic objects fails in general environments where uncontrolled occlusions are commonplace, due to inherent silhouette corruption by occluders. We show that occluders ...Conference Paper -
OpenScene: 3D Scene Understanding with Open Vocabularies
(2023)2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Traditional 3D scene understanding approaches rely on labeled 3D datasets to train a model for a single task with supervision. We propose OpenScene, an alternative approach where a model predicts dense features for 3D scene points that are co-embedded with text and image pixels in CLIP feature space. This zero-shot approach enables task-agnostic training and open-vocabulary queries. For example, to perform SOTA zero-shot 3D semantic ...Conference Paper -
SFly: Swarm of micro flying robots
(2012)2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012)The SFly project is an EU-funded project, with the goal to create a swarm of autonomous vision controlled micro aerial vehicles. The mission in mind is that a swarm of MAV's autonomously maps out an unknown environment, computes optimal surveillance positions and places the MAV's there and then locates radio beacons in this environment. The scope of the work includes contributions on multiple different levels ranging from theoretical ...Conference Paper -
Four-view Geometry with Unknown Radial Distortion
(2023)2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)We present novel solutions to previously unsolved problems of relative pose estimation from images whose calibration parameters, namely focal lengths and radial distortion, are unknown. Our approach enables metric reconstruction without modeling these parameters. The minimal case for reconstruction requires 13 points in 4 views for both the calibrated and uncalibrated cameras. We describe and implement the first solution to these minimal ...Conference Paper -
Removing Objects From Neural Radiance Fields
(2023)2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Before sharing a NeRF, though, it might be desirable to remove personal information or unsightly objects. Such removal is not easily achieved with the current NeRF editing frameworks. We propose a framework to remove objects from a NeRF representation created from ...Conference Paper -
DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients
(2023)2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Line segments are ubiquitous in our human-made world and are increasingly used in vision tasks. They are complementary to feature points thanks to their spatial extent and the structural information they provide. Traditional line detectors based on the image gradient are extremely fast and accurate, but lack robustness in noisy images and challenging conditions. Their learned counterparts are more repeatable and can handle challenging ...Conference Paper -
3D Line Mapping Revisited
(2023)2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)In contrast to sparse keypoints, a handful of line segments can concisely encode the high-level scene layout, as they often delineate the main structural elements. In addition to offering strong geometric cues, they are also omnipresent in urban landscapes and indoor scenes. Despite their apparent advantages, current line-based reconstruction methods are far behind their point-based counterparts. In this paper we aim to close the gap by ...Conference Paper -
VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction
(2023)2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)The success of the Neural Radiance Fields (NeRF) in novel view synthesis has inspired researchers to propose neural implicit scene reconstruction. However, most existing neural implicit reconstruction methods optimize perscene parameters and therefore lack generalizability to new scenes. We introduce VolRecon, a novel generalizable implicit reconstruction method with Signed Ray Distance Function (SRDF). To reconstruct the scene with fine ...Conference Paper -
A Cross-Season Correspondence Dataset for Robust Semantic Segmentation
(2019)Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)Conference Paper