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 151–160 of 1576 papers

TitleStatusHype
Efficient 3D Perception on Multi-Sweep Point Cloud with Gumbel Spatial Pruning—0
LSSInst: Improving Geometric Modeling in LSS-Based BEV Perception with Instance RepresentationCode1
ZOPP: A Framework of Zero-shot Offboard Panoptic Perception for Autonomous Driving—0
SimpleBEV: Improved LiDAR-Camera Fusion Architecture for 3D Object Detection—0
Efficient Feature Aggregation and Scale-Aware Regression for Monocular 3D Object DetectionCode1
CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object DetectionCode1
Self-supervised cross-modality learning for uncertainty-aware object detection and recognition in applications which lack pre-labelled training data—0
One for All: Multi-Domain Joint Training for Point Cloud Based 3D Object Detection—0
GAFusion: Adaptive Fusing LiDAR and Camera with Multiple Guidance for 3D Object Detection—0
ImOV3D: Learning Open-Vocabulary Point Clouds 3D Object Detection from Only 2D ImagesCode2
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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