SOTAVerified

3D Object Detection

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 )

Papers

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Showing 1051–1075 of 1576 papers

TitleStatusHype
Exploring Geometry-Aware Contrast and Clustering Harmonization for Self-Supervised 3D Object Detection—0
Exploring the Capabilities and Limits of 3D Monocular Object Detection -- A Study on Simulation and Real World Data—0
Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation—0
FALO: Fast and Accurate LiDAR 3D Object Detection on Resource-Constrained Devices—0
False Positive Sampling-based Data Augmentation for Enhanced 3D Object Detection Accuracy—0
FastOcc: Accelerating 3D Occupancy Prediction by Fusing the 2D Bird's-Eye View and Perspective View—0
Fast Point R-CNN—0
FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection—0
F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds—0
Feature Compression for Cloud-Edge Multimodal 3D Object Detection—0
FFPA-Net: Efficient Feature Fusion with Projection Awareness for 3D Object Detection—0
FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection—0
Finetuning Pre-trained Model with Limited Data for LiDAR-based 3D Object Detection by Bridging Domain Gaps—0
Floating Car Observers in Intelligent Transportation Systems: Detection Modeling and Temporal Insights—0
Focal-PETR: Embracing Foreground for Efficient Multi-Camera 3D Object Detection—0
Frame Fusion with Vehicle Motion Prediction for 3D Object Detection—0
From Dataset to Real-world: General 3D Object Detection via Generalized Cross-domain Few-shot Learning—0
Frustum Fusion: Pseudo-LiDAR and LiDAR Fusion for 3D Detection—0
Frustum VoxNet for 3D object detection from RGB-D or Depth images—0
FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels—0
FSHNet: Fully Sparse Hybrid Network for 3D Object Detection—0
Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images—0
Towards Long-Range 3D Object Detection for Autonomous Vehicles—0
Fusing Bird View LIDAR Point Cloud and Front View Camera Image for Deep Object Detection—0
FusionFormer: A Multi-sensory Fusion in Bird's-Eye-View and Temporal Consistent Transformer for 3D Object Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1EA-LSSNDS0.78—Unverified
2MMFusion-eNDS0.77—Unverified
3MegFusionNDS0.77—Unverified
4RacoonPowerNDS0.76—Unverified
5BEVFusion-eNDS0.76—Unverified
6DeepInteraction-largeNDS0.76—Unverified
7DeepInteraction-eNDS0.76—Unverified
8DAANDS0.75—Unverified
9FusionVPENDS0.75—Unverified
10CenterPoint-FusionNDS0.75—Unverified