3D Object Detection
585 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
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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
Scaling Multi-Camera 3D Object Detection through Weak-to-Strong Eliciting
Finally, for MC3D-Det joint training, the elaborate dataset merge strategy is designed to solve the problem of inconsistent camera numbers and camera parameters.
Better Monocular 3D Detectors with LiDAR from the Past
Accurate 3D object detection is crucial to autonomous driving.
MonoCD: Monocular 3D Object Detection with Complementary Depths
Monocular 3D object detection has attracted widespread attention due to its potential to accurately obtain object 3D localization from a single image at a low cost.
HENet: Hybrid Encoding for End-to-end Multi-task 3D Perception from Multi-view Cameras
Three-dimensional perception from multi-view cameras is a crucial component in autonomous driving systems, which involves multiple tasks like 3D object detection and bird's-eye-view (BEV) semantic segmentation.
VSRD: Instance-Aware Volumetric Silhouette Rendering for Weakly Supervised 3D Object Detection
skmhrk1209/VSRD
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In the auto-labeling stage, we represent the surface of each instance as a signed distance field (SDF) and render its silhouette as an instance mask through our proposed instance-aware volumetric silhouette rendering.
Weak-to-Strong 3D Object Detection with X-Ray Distillation
This paper addresses the critical challenges of sparsity and occlusion in LiDAR-based 3D object detection.
SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects
We argue that the cause of failure is the sensitivity of depth regression losses to noise of larger objects.
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
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The robustness of driving perception systems under unprecedented conditions is crucial for safety-critical usages.
