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 976–1000 of 1576 papers

TitleStatusHype
LargeKernel3D: Scaling up Kernels in 3D Sparse CNNsCode2
BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object DetectionCode2
Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy AutoencodersCode2
3D Object Detection for Autonomous Driving: A Comprehensive SurveyCode2
K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather ConditionsCode2
Real3D-Aug: Point Cloud Augmentation by Placing Real Objects with Occlusion Handling for 3D Detection and SegmentationCode1
MonoGround: Detecting Monocular 3D Objects from the GroundCode1
LinK3D: Linear Keypoints Representation for 3D LiDAR Point CloudCode2
Learning Ego 3D Representation as Ray TracingCode1
Delving into the Pre-training Paradigm of Monocular 3D Object Detection—0
SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving—0
PETRv2: A Unified Framework for 3D Perception from Multi-Camera ImagesCode3
SparseDet: Towards End-to-End 3D Object Detection—0
Unifying Voxel-based Representation with Transformer for 3D Object DetectionCode2
LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection—0
Voxel Field Fusion for 3D Object DetectionCode1
itKD: Interchange Transfer-based Knowledge Distillation for 3D Object DetectionCode1
Towards Efficient 3D Object Detection with Knowledge DistillationCode1
Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object DetectionCode1
Time3D: End-to-End Joint Monocular 3D Object Detection and Tracking for Autonomous Driving—0
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-trainingCode2
Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images—0
BEVFusion: A Simple and Robust LiDAR-Camera Fusion FrameworkCode2
BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View RepresentationCode4
Structure Aware and Class Balanced 3D Object Detection on nuScenes Dataset—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