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 801–825 of 1576 papers

TitleStatusHype
Clusterformer: Cluster-based Transformer for 3D Object Detection in Point Clouds—0
A Simple Vision Transformer for Weakly Semi-supervised 3D Object Detection—0
CoIn: Contrastive Instance Feature Mining for Outdoor 3D Object Detection with Very Limited AnnotationsCode1
FocalFormer3D: Focusing on Hard Instance for 3D Object DetectionCode2
ObjectFusion: Multi-modal 3D Object Detection with Object-Centric Fusion—0
AShapeFormer: Semantics-Guided Object-Level Active Shape Encoding for 3D Object Detection via TransformersCode0
Implicit Surface Contrastive Clustering for LiDAR Point Clouds—0
Semi-Supervised Stereo-Based 3D Object Detection via Cross-View Consensus—0
Deep Dive Into Gradients: Better Optimization for 3D Object Detection With Gradient-Corrected IoU SupervisionCode1
X3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection—0
Benchmarking Robustness of 3D Object Detection to Common CorruptionsCode1
Distilling Focal Knowledge From Imperfect Expert for 3D Object DetectionCode0
LiDAR-in-the-Loop Hyperparameter Optimization—0
TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry LearningCode1
Monocular 3D Object Detection using Multi-Stage Approaches with Attention and Slicing aided hyper inference—0
OBMO: One Bounding Box Multiple Objects for Monocular 3D Object DetectionCode0
A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial AttacksCode1
Learning Object-level Point Augmentor for Semi-supervised 3D Object DetectionCode1
Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy CommunicationCode1
Multi-level and multi-modal feature fusion for accurate 3D object detection in Connected and Automated Vehicles—0
DETR4D: Direct Multi-View 3D Object Detection with Sparse Attention—0
MAELi: Masked Autoencoder for Large-Scale LiDAR Point Clouds—0
ConQueR: Query Contrast Voxel-DETR for 3D Object DetectionCode1
VINet: Lightweight, Scalable, and Heterogeneous Cooperative Perception for 3D Object Detection—0
MegaPose: 6D Pose Estimation of Novel Objects via Render & CompareCode2
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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