Search Results for author:
Found 11 papers, 9 papers with code
How Effective is Pre-training of Large Masked Autoencoders for Downstream Earth Observation Tasks?
We evaluate Prithvi on reconstruction and segmentation-based downstream tasks, and for SatMAE we assess its performance on a classification downstream task.
UniDet3D: Multi-dataset Indoor 3D Object Detection
Growing customer demand for smart solutions in robotics and augmented reality has attracted considerable attention to 3D object detection from point clouds.
Ranked #1 on
3D Object Detection
on ScanNet++
(using extra training data)
OneFormer3D: One Transformer for Unified Point Cloud Segmentation
Semantic, instance, and panoptic segmentation of 3D point clouds have been addressed using task-specific models of distinct design.
Ranked #1 on
Panoptic Segmentation
on ScanNet
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 #5 on
3D Instance Segmentation
on S3DIS
(using extra training data)
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 ScanNet++
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 #3 on
3D Object Detection
on ScanNet++
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 #2 on
Monocular 3D Object Detection
on SUN RGB-D
Monocular 3D Object Detection
Monocular 3D Object Detection (10 / NYU-37)
+4
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.
