3D Object Detection
590 papers with code • 55 benchmarks • 48 datasets
3D Object Detection is a task in computer vision where the goal is to identify and locate objects in a 3D environment based on their shape, location, and orientation. It involves detecting the presence of objects and determining their location in the 3D space in real-time. This task is crucial for applications such as autonomous vehicles, robotics, and augmented reality.
( Image credit: AVOD )
Benchmarks
These leaderboards are used to track progress in 3D Object Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find 3D Object Detection models and implementations
KITTI
nuScenes
ScanNet
NYUv2
SUN RGB-D
S3DIS
Waymo Open Dataset
Argoverse
Argoverse 2
ONCE
Subtasks
Latest papers
UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps
In this study, we address a gap in existing unsupervised domain adaptation approaches on LiDAR-based 3D object detection, which have predominantly concentrated on adapting between established, high-density autonomous driving datasets.
RCBEVDet: Radar-camera Fusion in Bird's Eye View for 3D Object Detection
In the dual-stream radar backbone, a point-based encoder and a transformer-based encoder are proposed to extract radar features, with an injection and extraction module to facilitate communication between the two encoders.
Optimizing LiDAR Placements for Robust Driving Perception in Adverse Conditions
ywyeli/place3d
•
•
The robustness of driving perception systems under unprecedented conditions is crucial for safety-critical usages.
IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection
HSF applies Point-to-Grid and Grid-to-Region transformers to capture the multimodal scene context at different granularities.
RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception
The value of roadside perception, which could extend the boundaries of autonomous driving and traffic management, has gradually become more prominent and acknowledged in recent years.
MIM4D: Masked Modeling with Multi-View Video for Autonomous Driving Representation Learning
Learning robust and scalable visual representations from massive multi-view video data remains a challenge in computer vision and autonomous driving.
Unleashing HyDRa: Hybrid Fusion, Depth Consistency and Radar for Unified 3D Perception
phi-wol/hydra
•
•
Low-cost, vision-centric 3D perception systems for autonomous driving have made significant progress in recent years, narrowing the gap to expensive LiDAR-based methods.
3D Semantic Segmentation-Driven Representations for 3D Object Detection
In autonomous driving, 3D detection provides more precise information to downstream tasks, including path planning and motion estimation, compared to 2D detection.
Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid for 3D Object Detection
konyul/FG-PFE
•
•
Through STV grids, points within each pillar are individually encoded using Vertical PFE (V-PFE), Temporal PFE (T-PFE), and Horizontal PFE (H-PFE).
SAFDNet: A Simple and Effective Network for Fully Sparse 3D Object Detection
LiDAR-based 3D object detection plays an essential role in autonomous driving.
