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 801–850 of 1576 papers

TitleStatusHype
Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images—0
Delving into the Pre-training Paradigm of Monocular 3D Object Detection—0
Are we Missing Confidence in Pseudo-LiDAR Methods for Monocular 3D Object Detection?—0
DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network—0
Dense Voxel Fusion for 3D Object Detection—0
Density-Based Clustering for 3D Object Detection in Point Clouds—0
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization—0
Depth-aware Fusion Method based on Image and 4D Radar Spectrum for 3D Object Detection—0
Depth-discriminative Metric Learning for Monocular 3D Object Detection—0
DepthFusion: Depth-Aware Hybrid Feature Fusion for LiDAR-Camera 3D Object Detection—0
DETR4D: Direct Multi-View 3D Object Detection with Sparse Attention—0
DetVPCC: RoI-based Point Cloud Sequence Compression for 3D Object Detection—0
Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection—0
DiffRef3D: A Diffusion-based Proposal Refinement Framework for 3D Object Detection—0
DifFUSER: Diffusion Model for Robust Multi-Sensor Fusion in 3D Object Detection and BEV Segmentation—0
Diffusion-based 3D Object Detection with Random Boxes—0
Digging Into Output Representation for Monocular 3D Object Detection—0
Dimension Embeddings for Monocular 3D Object Detection—0
Directed-CP: Directed Collaborative Perception for Connected and Autonomous Vehicles via Proactive Attention—0
DirectShape: Direct Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation—0
DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment—0
DisARM: Displacement Aware Relation Module for 3D Detection—0
Disentangling and Vectorization: A 3D Visual Perception Approach for Autonomous Driving Based on Surround-View Fisheye Cameras—0
Disentangling Monocular 3D Object Detection—0
Distance-Normalized Unified Representation for Monocular 3D Object Detection—0
Distilling Temporal Knowledge with Masked Feature Reconstruction for 3D Object Detection—0
Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection—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
Domain Adaptation in 3D Object Detection with Gradual Batch Alternation Training—0
DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes—0
Doracamom: Joint 3D Detection and Occupancy Prediction with Multi-view 4D Radars and Cameras for Omnidirectional Perception—0
DPointNet: A Density-Oriented PointNet for 3D Object Detection in Point Clouds—0
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving—0
Drosophila-Inspired 3D Moving Object Detection Based on Point Clouds—0
Dual-Domain Homogeneous Fusion with Cross-Modal Mamba and Progressive Decoder for 3D Object Detection—0
Dual LiDAR-Based Traffic Movement Count Estimation at a Signalized Intersection: Deployment, Data Collection, and Preliminary Analysis—0
DuEqNet: Dual-Equivariance Network in Outdoor 3D Object Detection for Autonomous Driving—0
DuoSpaceNet: Leveraging Both Bird's-Eye-View and Perspective View Representations for 3D Object Detection—0
DV-Det: Efficient 3D Point Cloud Object Detection with Dynamic Voxelization—0
Dynamic Edge Weights in Graph Neural Networks for 3D Object Detection—0
Dynamic V2X Autonomous Perception from Road-to-Vehicle Vision—0
DynStatF: An Efficient Feature Fusion Strategy for LiDAR 3D Object Detection—0
DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos—0
Efficient 3D Perception on Multi-Sweep Point Cloud with Gumbel Spatial Pruning—0
Efficient On-Chip Implementation of 4D Radar-Based 3D Object Detection on Hailo-8L—0
Efficient Transformer-based 3D Object Detection with Dynamic Token Halting—0
EffiPerception: an Efficient Framework for Various Perception Tasks—0
ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection—0
Ego-Motion Estimation and Dynamic Motion Separation from 3D Point Clouds for Accumulating Data and Improving 3D Object Detection—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