Search Results for author:
Found 15 papers, 5 papers with code
INF: Implicit Neural Fusion for LiDAR and Camera
INF first trains a neural density field of the target scene using LiDAR frames.
Hierarchical Neural Memory Network for Low Latency Event Processing
Conventional architectures encode entire scene contents at a fixed rate regardless of their temporal characteristics.
Ranked #2 on
Object Detection
on GEN1 Detection
Heterogeneous Grid Convolution for Adaptive, Efficient, and Controllable Computation
This paper proposes a novel heterogeneous grid convolution that builds a graph-based image representation by exploiting heterogeneity in the image content, enabling adaptive, efficient, and controllable computations in a convolutional architecture.
Epipolar-Guided Deep Object Matching for Scene Change Detection
To cope with the difficulty, we introduce a deep graph matching network that establishes object correspondence between an image pair.
Privacy Preserving Visual SLAM
This study proposes a privacy-preserving Visual SLAM framework for estimating camera poses and performing bundle adjustment with mixed line and point clouds in real time.
SOIC: Semantic Online Initialization and Calibration for LiDAR and Camera
This paper presents a novel semantic-based online extrinsic calibration approach, SOIC (so, I see), for Light Detection and Ranging (LiDAR) and camera sensors.
OpenVSLAM: A Versatile Visual SLAM Framework
In this paper, we introduce OpenVSLAM, a visual SLAM framework with high usability and extensibility.
TriDepth: Triangular Patch-based Deep Depth Prediction
We propose a novel and efficient representation for single-view depth estimation using Convolutional Neural Networks (CNNs).
Rare Event Detection using Disentangled Representation Learning
The effectiveness of the proposed approach is verified by the quantitative evaluations on four change detection datasets, and the qualitative analysis shows that the proposed method can acquire the representations that disentangle rare events from trivial ones.
Weakly Supervised Silhouette-based Semantic Scene Change Detection
A straightforward approach for this task is to train a semantic change detection network directly from a large-scale dataset in an end-to-end manner.
Scale Estimation of Monocular SfM for a Multi-modal Stereo Camera
This paper proposes a novel method of estimating the absolute scale of monocular SfM for a multi-modal stereo camera.
Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints
This paper presents a novel method for detecting scene changes from a pair of images with a difference of camera viewpoints using a dense optical flow based change detection network.
Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets
The networks are trained to output images that are close to the ground truth using the images synthesized with clouds over the ground truth as inputs.
Ranked #8 on
Cloud Removal
on SEN12MS-CR
Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard
Once the corners of the chessboard in the 3D point cloud are estimated, the extrinsic calibration of the two sensors is converted to a 3D-2D matching problem.
Detecting Changes in 3D Structure of a Scene from Multi-view Images Captured by a Vehicle-Mounted Camera
The proposed method is compared with the methods that use multi-view stereo (MVS) to reconstruct the scene structures of the two time points and then differentiate them to detect changes.
