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

Showing 401425 of 1576 papers

TitleStatusHype
SRCN3D: Sparse R-CNN 3D for Compact Convolutional Multi-View 3D Object Detection and TrackingCode1
MonoGround: Detecting Monocular 3D Objects from the GroundCode1
Real3D-Aug: Point Cloud Augmentation by Placing Real Objects with Occlusion Handling for 3D Detection and SegmentationCode1
Learning Ego 3D Representation as Ray TracingCode1
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
PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D DetectionCode1
3D Object Detection with a Self-supervised Lidar Scene Flow BackboneCode1
PolyLoss: A Polynomial Expansion Perspective of Classification Loss FunctionsCode1
Spatiality-guided Transformer for 3D Dense Captioning on Point CloudsCode1
Multimodal Token Fusion for Vision TransformersCode1
OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR DataCode1
DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D DetectorsCode1
RBGNet: Ray-based Grouping for 3D Object DetectionCode1
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded ScenesCode1
ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Regression FrameworkCode1
Homography Loss for Monocular 3D Object DetectionCode1
Fusing Event-based and RGB camera for Robust Object Detection in Adverse ConditionsCode1
LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object DetectionCode1
Point2Seq: Detecting 3D Objects as SequencesCode1
Hindsight is 20/20: Leveraging Past Traversals to Aid 3D PerceptionCode1
MonoDTR: Monocular 3D Object Detection with Depth-Aware TransformerCode1
Stereo Neural Vernier CaliperCode1
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Benchmark Results

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