3D Object Detection
591 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.
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Libraries
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Latest papers
Reliable Student: Addressing Noise in Semi-Supervised 3D Object Detection
Semi-supervised 3D object detection can benefit from the promising pseudo-labeling technique when labeled data is limited.
Commonsense Prototype for Outdoor Unsupervised 3D Object Detection
The prevalent approaches of unsupervised 3D object detection follow cluster-based pseudo-label generation and iterative self-training processes.
Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object Detection
LiDAR-based 3D object detection has become an essential part of automated driving due to its ability to localize and classify objects precisely in 3D.
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.
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.
VSRD: Instance-Aware Volumetric Silhouette Rendering for Weakly Supervised 3D Object Detection
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.