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 901–925 of 1576 papers

TitleStatusHype
Boosting 3D Object Detection with Semantic-Aware Multi-Branch Framework—0
Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey—0
Boosting Monocular 3D Object Detection with Object-Centric Auxiliary Depth Supervision—0
Boosting Single-Frame 3D Object Detection by Simulating Multi-Frame Point Clouds—0
Boundary-Aware Dense Feature Indicator for Single-Stage 3D Object Detection from Point Clouds—0
BOX3D: Lightweight Camera-LiDAR Fusion for 3D Object Detection and Localization—0
Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detection—0
Bridging the View Disparity Between Radar and Camera Features for Multi-modal Fusion 3D Object Detection—0
BugNIST -- a Large Volumetric Dataset for Object Detection under Domain Shift—0
CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception—0
Can We Remove the Ground? Obstacle-aware Point Cloud Compression for Remote Object Detection—0
Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving—0
CARLA Drone: Monocular 3D Object Detection from a Different Perspective—0
CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection—0
CatFree3D: Category-agnostic 3D Object Detection with Diffusion—0
Center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding—0
Center Feature Fusion: Selective Multi-Sensor Fusion of Center-based Objects—0
CenterRadarNet: Joint 3D Object Detection and Tracking Framework using 4D FMCW Radar—0
Cirrus: A Long-range Bi-pattern LiDAR Dataset—0
Class-Aware PillarMix: Can Mixed Sample Data Augmentation Enhance 3D Object Detection with Radar Point Clouds?—0
CleverDistiller: Simple and Spatially Consistent Cross-modal Distillation—0
CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow—0
Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts—0
Clusterformer: Cluster-based Transformer for 3D Object Detection in Point Clouds—0
ClusterFusion: Leveraging Radar Spatial Features for Radar-Camera 3D Object Detection in Autonomous Vehicles—0
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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