3D Multi-Object Tracking
31 papers with code • 6 benchmarks • 7 datasets
Image: Weng et al
Benchmarks
These leaderboards are used to track progress in 3D Multi-Object Tracking
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
KITTI
nuScenes
Waymo Open Dataset
nuScenes LiDAR only
MOTFront
3D-ZeF
CVB
Latest papers with no code
CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion
As such, we propose a novel camera-LiDAR fusion 3D MOT framework based on the Combined Appearance-Motion Optimization (CAMO-MOT), which uses both camera and LiDAR data and significantly reduces tracking failures caused by occlusion and false detection.
Quality Matters: Embracing Quality Clues for Robust 3D Multi-Object Tracking
Recent advanced works generally employ a series of object attributes, e. g., position, size, velocity, and appearance, to provide the clues for the association in 3D MOT.
InterTrack: Interaction Transformer for 3D Multi-Object Tracking
We then perform a learned regression on each track/detection feature pair to estimate affinities, and use a robust two-stage data association and track management approach to produce the final tracks.
PolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking?
This allows our graph neural network to learn to effectively encode temporal and spatial interactions and fully leverage contextual and motion cues to obtain final scene interpretation by posing data association as edge classification.
SpOT: Spatiotemporal Modeling for 3D Object Tracking
In this work, we develop a holistic representation of traffic scenes that leverages both spatial and temporal information of the actors in the scene.
3D Multi-Object Tracking with Differentiable Pose Estimation
We propose a novel approach for joint 3D multi-object tracking and reconstruction from RGB-D sequences in indoor environments.
3D Multi-Object Tracking Using Graph Neural Networks with Cross-Edge Modality Attention
We evaluate our approach using various sensor modalities and model configurations on the challenging nuScenes and KITTI datasets.
DetFlowTrack: 3D Multi-object Tracking based on Simultaneous Optimization of Object Detection and Scene Flow Estimation
we proposed a 3D MOT framework based on simultaneous optimization of object detection and scene flow estimation.
CFTrack: Center-based Radar and Camera Fusion for 3D Multi-Object Tracking
Our proposed method uses a center-based radar-camera fusion algorithm for object detection and utilizes a greedy algorithm for object association.
TesseTrack: End-to-End Learnable Multi-Person Articulated 3D Pose Tracking
At the core of our approach is a novel spatio-temporal formulation that operates in a common voxelized feature space aggregated from single- or multiple camera views.
