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 551–575 of 1576 papers

TitleStatusHype
Part-Aware Data Augmentation for 3D Object Detection in Point CloudCode1
Weakly Supervised 3D Object Detection from Lidar Point CloudCode1
Pillar-based Object Detection for Autonomous DrivingCode1
Kinematic 3D Object Detection in Monocular VideoCode1
EPNet: Enhancing Point Features with Image Semantics for 3D Object DetectionCode1
CenterNet3D: An Anchor Free Object Detector for Point CloudCode1
Wasserstein Distances for Stereo Disparity EstimationCode1
AFDet: Anchor Free One Stage 3D Object DetectionCode1
H3DNet: 3D Object Detection Using Hybrid Geometric PrimitivesCode1
IDA-3D: Instance-Depth-Aware 3D Object Detection From Stereo Vision for Autonomous DrivingCode1
3D-MPA: Multi-Proposal Aggregation for 3D Semantic Instance SegmentationCode1
Structure Aware Single-Stage 3D Object Detection From Point CloudCode1
Range Conditioned Dilated Convolutions for Scale Invariant 3D Object DetectionCode1
Train in Germany, Test in The USA: Making 3D Object Detectors GeneralizeCode1
3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object DetectionCode1
MLCVNet: Multi-Level Context VoteNet for 3D Object DetectionCode1
End-to-End Pseudo-LiDAR for Image-Based 3D Object DetectionCode1
Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity EstimationCode1
SSN: Shape Signature Networks for Multi-class Object Detection from Point CloudsCode1
Finding Your (3D) Center: 3D Object Detection Using a Learned LossCode1
3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance SegmentationCode1
Predicting Semantic Map Representations from Images using Pyramid Occupancy NetworksCode1
MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View MapsCode1
BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye ViewCode1
Point-GNN: Graph Neural Network for 3D Object Detection in a Point CloudCode1
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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