Search Results for author:
Found 38 papers, 25 papers with code
3D Hierarchical Panoptic Segmentation in Real Orchard Environments Across Different Sensors
In this paper, we introduce a novel approach to address the problem of hierarchical panoptic segmentation of apple orchards on 3D data from different sensors.
PINGS: Gaussian Splatting Meets Distance Fields within a Point-Based Implicit Neural Map
In this paper, we propose a novel map representation that unifies a continuous signed distance field and a Gaussian splatting radiance field within an elastic and compact point-based implicit neural map.
ActiveGS: Active Scene Reconstruction using Gaussian Splatting
The core of our framework is an effective confidence modelling technique for the Gaussian splatting map to identify under-reconstructed areas, while utilising spatial information from the voxel map to target unexplored areas and assist in collision-free path planning.
Open-World Panoptic Segmentation
Additionally, we propose PANIC (Panoptic ANomalies In Context), a benchmark for evaluating open-world panoptic segmentation in autonomous driving scenarios.
Epipolar Attention Field Transformers for Bird's Eye View Semantic Segmentation
Spatial understanding of the semantics of the surroundings is a key capability needed by autonomous cars to enable safe driving decisions.
Horticultural Temporal Fruit Monitoring via 3D Instance Segmentation and Re-Identification using Point Clouds
Experimental results on a real dataset of strawberries demonstrate that our approach outperforms other methods for fruits re-identification over time, allowing for precise temporal fruit monitoring in real and complex scenarios.
AdaCropFollow: Self-Supervised Online Adaptation for Visual Under-Canopy Navigation
A large number of failures of this system can be attributed to the inability of the perception model to adapt to the domain shift encountered during deployment.
HeLiMOS: A Dataset for Moving Object Segmentation in 3D Point Clouds From Heterogeneous LiDAR Sensors
Finally, we provide experimental results regarding the performance of commonly used state-of-the-art MOS approaches on HeLiMOS that suggest a new direction for a sensor-agnostic MOS, which generally works regardless of the type of LiDAR sensors used to capture 3D point clouds.
A Dataset and Benchmark for Shape Completion of Fruits for Agricultural Robotics
For acquiring the ground truth shape, we developed a measuring process that allows us to record data of real sweet pepper plants, both in the lab and in the greenhouse with high precision, and determine the shape of the sensed fruits.
3D LiDAR Mapping in Dynamic Environments Using a 4D Implicit Neural Representation
We propose a novel approach for building accurate maps of dynamic environments utilizing a sequence of LiDAR scans.
Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion
Our experimental evaluation shows that our method can complete the scene given a single LiDAR scan as input, producing a scene with more details compared to state-of-the-art scene completion methods.
Open-World Semantic Segmentation Including Class Similarity
Interpreting camera data is key for autonomously acting systems, such as autonomous vehicles.
PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency
In this paper, we propose a SLAM system for building globally consistent maps, called PIN-SLAM, that is based on an elastic and compact point-based implicit neural map representation.
Unsupervised Pre-Training for 3D Leaf Instance Segmentation
Monitoring plants and measuring their traits is an important task in agriculture often referred to as plant phenotyping.
BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions
Agricultural production is facing severe challenges in the next decades induced by climate change and the need for sustainability, reducing its impact on the environment.
Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds
In this paper, we address the problem of moving instance segmentation in radar point clouds to enhance scene interpretation for safety-critical tasks.
Mask4D: End-to-End Mask-Based 4D Panoptic Segmentation for LiDAR Sequences
Panoptic segmentation of 3D LiDAR scans allows us to semantically describe a vehicle’s environment by predicting semantic classes for each 3D point and to identify individual instances through different instance IDs.
Ranked #3 on
4D Panoptic Segmentation
on SemanticKITTI
PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain
Our dataset, benchmarks, and code are available at \url{https://www. phenobench. org}.
On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics
Furthermore, the pre-trained networks obtain similar performance to the fully supervised with less labeled data.
Constructing Metric-Semantic Maps using Floor Plan Priors for Long-Term Indoor Localization
We exploit 3D object detections from monocular RGB frames for both, the object-based map construction, and for globally localizing in the constructed map.
Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots
Monitoring plants and fruits at high resolution play a key role in the future of agriculture.
Temporal Consistent 3D LiDAR Representation Learning for Semantic Perception in Autonomous Driving
Especially in autonomous driving, point clouds are sparse, and objects appearance depends on its distance from the sensor, making it harder to acquire large amounts of labeled training data.
Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data
Scene understanding is crucial for autonomous robots in dynamic environments for making future state predictions, avoiding collisions, and path planning.
Fully On-board Low-Power Localization with Multizone Time-of-Flight Sensors on Nano-UAVs
Experimental evaluation using a nano-UAV open platform demonstrated that the proposed solution is capable of localizing on a 31. 2m$\boldsymbol{^2}$ map with 0. 15m accuracy and an above 95% success rate.
Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain
In this paper, we address the problem of joint semantic, plant instance, and leaf instance segmentation of crop fields from RGB data.
Robust Double-Encoder Network for RGB-D Panoptic Segmentation
We propose a novel encoder-decoder neural network that processes RGB and depth separately through two encoders.
IR-MCL: Implicit Representation-Based Online Global Localization
The experiments suggest that the presented implicit representation is able to predict more accurate 2D LiDAR scans leading to an improved observation model for our particle filter-based localization.
SHINE-Mapping: Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representations
Accurate mapping of large-scale environments is an essential building block of most outdoor autonomous systems.
Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions
A key challenge for autonomous vehicles is to navigate in unseen dynamic environments.
Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans
We propose a novel approach that builds on top of an arbitrary single-scan panoptic segmentation network and extends it to the temporal domain by associating instances across time.
Ranked #5 on
4D Panoptic Segmentation
on SemanticKITTI
Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks
In this paper, we address the problem of predicting future 3D LiDAR point clouds given a sequence of past LiDAR scans.
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.
Ranked #7 on
4D Panoptic Segmentation
on SemanticKITTI
A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI
Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly.
RangeNet++: Fast and Accurate LiDAR Semantic Segmentation
Perception in autonomous vehicles is often carried out through a suite of different sensing modalities.
Ranked #19 on
Robust 3D Semantic Segmentation
on SemanticKITTI-C
ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals
For localization and mapping, we employ an efficient direct tracking on the truncated signed distance function (TSDF) and leverage color information encoded in the TSDF to estimate the pose of the sensor.
Robotics
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR.
Ranked #33 on
3D Semantic Segmentation
on SemanticKITTI
Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment in Precision Farming
It outputs the stem location for weeds, which allows for mechanical treatments, and the covered area of the weed for selective spraying.
Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming
Exploiting the crop arrangement information that is observable from the image sequences enables our system to robustly estimate a pixel-wise labeling of the images into crop and weed, i. e., a semantic segmentation.
