research
∙
09/18/2023
Pre-training on Synthetic Driving Data for Trajectory Prediction
Accumulating substantial volumes of real-world driving data proves pivot...
share
research
∙
08/08/2023
DELFlow: Dense Efficient Learning of Scene Flow for Large-Scale Point Clouds
Point clouds are naturally sparse, while image pixels are dense. The inc...
research
∙
07/27/2023
NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection
We present NeRF-Det, a novel method for indoor 3D detection with posed R...
research
∙
04/27/2023
SparseFusion: Fusing Multi-Modal Sparse Representations for Multi-Sensor 3D Object Detection
By identifying four important components of existing LiDAR-camera 3D obj...
research
∙
04/27/2023
Quadric Representations for LiDAR Odometry, Mapping and Localization
Current LiDAR odometry, mapping and localization methods leverage point-...
research
∙
04/03/2023
Open-Vocabulary Point-Cloud Object Detection without 3D Annotation
The goal of open-vocabulary detection is to identify novel objects based...
research
∙
10/05/2022
Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection
While recent camera-only 3D detection methods leverage multiple timestep...
research
∙
07/05/2022
Open-Vocabulary 3D Detection via Image-level Class and Debiased Cross-modal Contrastive Learning
Current point-cloud detection methods have difficulty detecting the open...
research
∙
04/21/2022
PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map
Deep learning has recently achieved significant progress in trajectory f...
research
∙
03/17/2022
DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection
While numerous 3D detection works leverage the complementary relationshi...
research
∙
07/19/2021
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Crowd counting on the drone platform is an interesting topic in computer...
research
∙
06/08/2021
Image2Point: 3D Point-Cloud Understanding with Pretrained 2D ConvNets
3D point-clouds and 2D images are different visual representations of th...
research
∙
04/06/2021
DCANet: Dense Context-Aware Network for Semantic Segmentation
As the superiority of context information gradually manifests in advance...
research
∙
03/18/2021
You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module
3D point-cloud-based perception is a challenging but crucial computer vi...
research
∙
03/06/2021
A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding
Detecting dynamic objects and predicting static road information such as...
research
∙
11/25/2020
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
We present Sparse R-CNN, a purely sparse method for object detection in ...
research
∙
06/05/2020
Visual Transformers: Token-based Image Representation and Processing for Computer Vision
Computer vision has achieved great success using standardized image repr...
research
∙
04/03/2020
SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation
LiDAR point-cloud segmentation is an important problem for many applicat...
research
∙
12/20/2019
AutoScale: Learning to Scale for Crowd Counting
Crowd counting in images is a widely explored but challenging task. Thou...
research
∙
07/29/2019
Learn to Scale: Generating Multipolar Normalized Density Map for Crowd Counting
Dense crowd counting aims to predict thousands of human instances from a...
research
∙
07/29/2019