Search Results for author:
Found 8 papers, 7 papers with code
TR3D: Towards Real-Time Indoor 3D Object Detection
Our model with early feature fusion, which we refer to as TR3D+FF, outperforms existing 3D object detection approaches on the SUN RGB-D dataset.
Ranked #2 on
3D Object Detection
on S3DIS
(using extra training data)
Top-Down Beats Bottom-Up in 3D Instance Segmentation
Most 3D instance segmentation methods exploit a bottom-up strategy, typically including resource-exhaustive post-processing.
Ranked #3 on
3D Instance Segmentation
on S3DIS
(using extra training data)
FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection
Existing 3D object detection methods make prior assumptions on the geometry of objects, and we argue that it limits their generalization ability.
Ranked #5 on
3D Object Detection
on S3DIS
ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection
To address this problem, we propose ImVoxelNet, a novel fully convolutional method of 3D object detection based on monocular or multi-view RGB images.
Ranked #1 on
Monocular 3D Object Detection
on SUN RGB-D
Learning High-Resolution Domain-Specific Representations with a GAN Generator
Based on this finding, we propose LayerMatch scheme for approximating the representation of a GAN generator that can be used for unsupervised domain-specific pretraining.
Semi-Supervised Semantic Segmentation
Vocal Bursts Intensity Prediction
IterDet: Iterative Scheme for Object Detection in Crowded Environments
Deep learning-based detectors usually produce a redundant set of object bounding boxes including many duplicate detections of the same object.
Ranked #1 on
Object Detection
on WiderPerson
MixMatch Domain Adaptaion: Prize-winning solution for both tracks of VisDA 2019 challenge
We present a domain adaptation (DA) system that can be used in multi-source and semi-supervised settings.
Estimation of Absolute Scale in Monocular SLAM Using Synthetic Data
This paper addresses the problem of scale estimation in monocular SLAM by estimating absolute distances between camera centers of consecutive image frames.
