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 1376–1400 of 1576 papers

TitleStatusHype
Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks—0
Recursive Cross-View: Use Only 2D Detectors to Achieve 3D Object Detection without 3D Annotations—0
R2Det: Redemption from Range-view for Accurate 3D Object Detection—0
RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving—0
Refining the ONCE Benchmark with Hyperparameter Tuning—0
Reflective Teacher: Semi-Supervised Multimodal 3D Object Detection in Bird's-Eye-View via Uncertainty Measure—0
Reinforced Axial Refinement Network for Monocular 3D Object Detection—0
Relation Graph Network for 3D Object Detection in Point Clouds—0
Reliability-Driven LiDAR-Camera Fusion for Robust 3D Object Detection—0
Representation Disparity-aware Distillation for 3D Object Detection—0
Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes—0
Rethinking Dimensionality Reduction in Grid-based 3D Object Detection—0
Reviewing 3D Object Detectors in the Context of High-Resolution 3+1D Radar—0
Revisiting 3D Object Detection From an Egocentric Perspective—0
Revisiting Cross-Domain Problem for LiDAR-based 3D Object Detection—0
Revisiting Monocular 3D Object Detection from Scene-Level Depth Retargeting to Instance-Level Spatial Refinement—0
RIDE: Boosting 3D Object Detection for LiDAR Point Clouds via Rotation-Invariant Analysis—0
RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud—0
ROA-BEV: 2D Region-Oriented Attention for BEV-based 3D Object—0
RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement—0
Robo3D: Towards Robust and Reliable 3D Perception against Corruptions—0
RobuRCDet: Enhancing Robustness of Radar-Camera Fusion in Bird's Eye View for 3D Object Detection—0
Robust 3D Object Detection in Cold Weather Conditions—0
Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and Outlook—0
ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape—0
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All datasetsnuScenesScanNetV2SUN-RGBD valKITTI Cars EasyKITTI Cars HardnuScenes Camera OnlyKITTI Cars Easy valKITTI Cars Moderate valnuscenes Camera-RadarView-of-Delft (val)KITTI Cars Hard valDAIR-V2X-I

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