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 701–725 of 1576 papers

TitleStatusHype
Drosophila-Inspired 3D Moving Object Detection Based on Point Clouds—0
An Empirical Analysis of Range for 3D Object Detection—0
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving—0
BOX3D: Lightweight Camera-LiDAR Fusion for 3D Object Detection and Localization—0
DPointNet: A Density-Oriented PointNet for 3D Object Detection in Point Clouds—0
MambaBEV: An efficient 3D detection model with Mamba2—0
LAM3D: Leveraging Attention for Monocular 3D Object Detection—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
MetaBEV: Solving Sensor Failures for 3D Detection and Map Segmentation—0
Boundary-Aware Dense Feature Indicator for Single-Stage 3D Object Detection from Point Clouds—0
Doracamom: Joint 3D Detection and Occupancy Prediction with Multi-view 4D Radars and Cameras for Omnidirectional Perception—0
DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes—0
Domain Adaptation in 3D Object Detection with Gradual Batch Alternation Training—0
Divide and Conquer: Improving Multi-Camera 3D Perception with 2D Semantic-Depth Priors and Input-Dependent Queries—0
DM3D: Distortion-Minimized Weight Pruning for Lossless 3D Object Detection—0
Boosting Single-Frame 3D Object Detection by Simulating Multi-Frame Point Clouds—0
Machine-learning based methodologies for 3d x-ray measurement, characterization and optimization for buried structures in advanced ic packages—0
Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection—0
Boosting Monocular 3D Object Detection with Object-Centric Auxiliary Depth Supervision—0
Boosting 3D Object Detection with Semantic-Aware Multi-Branch Framework—0
Distilling Temporal Knowledge with Masked Feature Reconstruction for 3D Object Detection—0
Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey—0
Distance-Normalized Unified Representation for Monocular 3D Object Detection—0
Moby: Empowering 2D Models for Efficient Point Cloud Analytics on the Edge—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