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 1201–1225 of 1576 papers

TitleStatusHype
TransPillars: Coarse-to-Fine Aggregation for Multi-Frame 3D Object Detection—0
Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions—0
Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds—0
Label-Guided Auxiliary Training Improves 3D Object DetectorCode0
Computer Vision Aided mmWave Beam Alignment in V2X CommunicationsCode0
On the Robustness of 3D Object Detectors—0
A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations—0
Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection—0
Paint and Distill: Boosting 3D Object Detection with Semantic Passing NetworkCode0
Real-Time And Robust 3D Object Detection with Roadside LiDARs—0
MT-Net Submission to the Waymo 3D Detection Leaderboard—0
Boosting Single-Frame 3D Object Detection by Simulating Multi-Frame Point Clouds—0
ORA3D: Overlap Region Aware Multi-view 3D Object Detection—0
Boosting 3D Object Detection by Simulating Multimodality on Point Clouds—0
Accurate and Real-time Pseudo Lidar Detection: Is Stereo Neural Network Really Necessary?—0
Self-Supervised 3D Monocular Object Detection by Recycling Bounding Boxes—0
LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network—0
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
SparseDet: Towards End-to-End 3D Object Detection—0
LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection—0
Time3D: End-to-End Joint Monocular 3D Object Detection and Tracking for Autonomous Driving—0
Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images—0
Structure Aware and Class Balanced 3D Object Detection on nuScenes Dataset—0
Robust 3D Object Detection in Cold Weather Conditions—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