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 501–525 of 1576 papers

TitleStatusHype
CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object DetectionCode1
FADet: A Multi-sensor 3D Object Detection Network based on Local Featured AttentionCode1
Progressive Coordinate Transforms for Monocular 3D Object DetectionCode1
Point Cloud Self-supervised Learning via 3D to Multi-view Masked AutoencoderCode1
Point Density-Aware Voxels for LiDAR 3D Object DetectionCode1
Far3D: Expanding the Horizon for Surround-view 3D Object DetectionCode1
Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using FusionCode1
FASTer: Focal Token Acquiring-and-Scaling Transformer for Long-term 3D Object DetectionCode1
Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color ContrastCode1
FastPillars: A Deployment-friendly Pillar-based 3D DetectorCode1
FSD-BEV: Foreground Self-Distillation for Multi-view 3D Object DetectionCode1
CoIn: Contrastive Instance Feature Mining for Outdoor 3D Object Detection with Very Limited AnnotationsCode1
Co-Fix3D: Enhancing 3D Object Detection with Collaborative RefinementCode1
Accurate 3D Object Detection using Energy-Based ModelsCode1
Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDARCode1
Fully Sparse Fusion for 3D Object DetectionCode1
CoDiff: Conditional Diffusion Model for Collaborative 3D Object DetectionCode1
FrustumFormer: Adaptive Instance-aware Resampling for Multi-view 3D DetectionCode1
Frustum PointNets for 3D Object Detection from RGB-D DataCode1
From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object DetectionCode1
From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point DecoderCode1
PolarBEVDet: Exploring Polar Representation for Multi-View 3D Object Detection in Bird's-Eye-ViewCode1
CoCoNets: Continuous Contrastive 3D Scene RepresentationsCode1
Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid for 3D Object DetectionCode1
A Simple Baseline for Multi-Camera 3D Object DetectionCode1
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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