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 1001–1025 of 1576 papers

TitleStatusHype
LiDAR-in-the-Loop Hyperparameter Optimization—0
LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection—0
LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception—0
LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network—0
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds—0
Lift-Attend-Splat: Bird's-eye-view camera-lidar fusion using transformers—0
LIFT: Learning 4D LiDAR Image Fusion Transformer for 3D Object Detection—0
Local Grid Rendering Networks for 3D Object Detection in Point Clouds—0
Adjusting Bias in Long Range Stereo Matching: A semantics guided approach—0
LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space Virtual Outlier Synthesis—0
LVIC: Multi-modality segmentation by Lifting Visual Info as Cue—0
LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion—0
LXLv2: Enhanced LiDAR Excluded Lean 3D Object Detection with Fusion of 4D Radar and Camera—0
M^2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation—0
M^2SODAI: Multi-Modal Maritime Object Detection Dataset With RGB and Hyperspectral Image Sensors—0
Machine-learning based methodologies for 3d x-ray measurement, characterization and optimization for buried structures in advanced ic packages—0
MAELi: Masked Autoencoder for Large-Scale LiDAR Point Clouds—0
MambaBEV: An efficient 3D detection model with Mamba2—0
MambaDETR: Query-based Temporal Modeling using State Space Model for Multi-View 3D Object Detection—0
MapFusion: A General Framework for 3D Object Detection with HDMaps—0
MaskBEV: Towards A Unified Framework for BEV Detection and Map Segmentation—0
MBDF-Net: Multi-Branch Deep Fusion Network for 3D Object Detection—0
MDS-Net: A Multi-scale Depth Stratification Based Monocular 3D Object Detection Algorithm—0
MetaBEV: Solving Sensor Failures for 3D Detection and Map Segmentation—0
Methodology for a Statistical Analysis of Influencing Factors on 3D Object Detection Performance—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