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Found 22 papers, 13 papers with code
Better Call SAL: Towards Learning to Segment Anything in Lidar
We propose $\texttt{SAL}$ ($\texttt{S}$egment $\texttt{A}$nything in $\texttt{L}$idar) method consisting of a text-promptable zero-shot model for segmenting and classifying any object in Lidar, and a pseudo-labeling engine that facilitates model training without manual supervision.
SeMoLi: What Moves Together Belongs Together
Recent results suggest that heuristic-based clustering methods in conjunction with object trackers can be used to pseudo-label instances of moving objects and use these as supervisory signals to train 3D object detectors in Lidar data without manual supervision.
Lidar Panoptic Segmentation and Tracking without Bells and Whistles
Our network is modular by design and optimized for all aspects of both the panoptic segmentation and tracking task.
Walking Your LiDOG: A Journey Through Multiple Domains for LiDAR Semantic Segmentation
To answer this question, we design the first experimental setup for studying domain generalization (DG) for LiDAR semantic segmentation (DG-LSS).
Pix2Map: Cross-modal Retrieval for Inferring Street Maps from Images
Moreover, we show that our retrieved maps can be used to update or expand existing maps and even show proof-of-concept results for visual localization and image retrieval from spatial graphs.
Learning to Discover and Detect Objects
We then train our network to learn to classify each RoI, either as one of the known classes, seen in the source dataset, or one of the novel classes, with a long-tail distribution constraint on the class assignments, reflecting the natural frequency of classes in the real world.
Ranked #2 on
Novel Object Detection
on LVIS v1.0 val
Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?
While we have significantly advanced short-term tracking performance, bridging longer occlusion gaps remains elusive: state-of-the-art object trackers only bridge less than 10% of occlusions longer than three seconds.
DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment
Direct methods have shown excellent performance in the applications of visual odometry and SLAM.
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.
Forecasting from LiDAR via Future Object Detection
Object detection and forecasting are fundamental components of embodied perception.
Opening Up Open World Tracking
A benchmark that would allow us to perform an apple-to-apple comparison of existing efforts is a crucial first step towards advancing this important research field.
Ranked #3 on
Open-World Video Segmentation
on BURST-val
(using extra training data)
(Just) A Spoonful of Refinements Helps the Registration Error Go Down
However, given the initial rotation estimate supplied by Kabsch, we show we can improve point correspondence learning during model training by extending the original optimization problem.
EagerMOT: 3D Multi-Object Tracking via Sensor Fusion
Multi-object tracking (MOT) enables mobile robots to perform well-informed motion planning and navigation by localizing surrounding objects in 3D space and time.
Ranked #1 on
3D Multi-Object Tracking
on KITTI
Opening up Open-World Tracking
We hope to open a new front in multi-object tracking research that will hopefully bring us a step closer to intelligent systems that can operate safely in the real world.
4D Panoptic LiDAR Segmentation
In this paper, we propose 4D panoptic LiDAR segmentation to assign a semantic class and a temporally-consistent instance ID to a sequence of 3D points.
STEP: Segmenting and Tracking Every Pixel
The task of assigning semantic classes and track identities to every pixel in a video is called video panoptic segmentation.
MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
We present MOTChallenge, a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data, and create a framework for the standardized evaluation of multiple object tracking methods.
Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation
Inspired by human navigation, we model the task of trajectory prediction as an intuitive two-stage process: (i) goal estimation, which predicts the most likely target positions of the agent, followed by a (ii) routing module which estimates a set of plausible trajectories that route towards the estimated goal.
Making a Case for 3D Convolutions for Object Segmentation in Videos
On the other hand, 3D convolutional networks have been successfully applied for video classification tasks, but have not been leveraged as effectively to problems involving dense per-pixel interpretation of videos compared to their 2D convolutional counterparts and lag behind the aforementioned networks in terms of performance.
Ranked #12 on
Unsupervised Video Object Segmentation
on DAVIS 2016 val
STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos
In this paper, we propose a different approach that is well-suited to a variety of tasks involving instance segmentation in videos.
Ranked #5 on
Unsupervised Video Object Segmentation
on DAVIS 2017 (val)
(using extra training data)
Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video
We explore object discovery and detector adaptation based on unlabeled video sequences captured from a mobile platform.
Track, then Decide: Category-Agnostic Vision-based Multi-Object Tracking
In this paper, we propose a model-free multi-object tracking approach that uses a category-agnostic image segmentation method to track objects.
