RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation

Milioto, Andres; Vizzo, Ignacio; Behley, Jens; Stachniss, Cyrill

doi:10.1109/iros40897.2019.8967762

Cited by 959 publications

(775 citation statements)

References 15 publications

Supporting

Mentioning

769

Contrasting

Unclassified

Order By: Relevance

“…We evaluate our mapping methods on all sequences with ground truth semantic labels. RangeNet++ [36] provides several pre-trained models and their predictions on Se-manticKITTI dataset. We choose SqueezesegV2 with K-Nearest Neighbor processing (SqueezesegV2-KNN) [36] to compute semantic measurements given the LiDAR points.…”

Section: B Semantickitti Datasetmentioning

confidence: 99%

“…RangeNet++ [36] provides several pre-trained models and their predictions on Se-manticKITTI dataset. We choose SqueezesegV2 with K-Nearest Neighbor processing (SqueezesegV2-KNN) [36] to compute semantic measurements given the LiDAR points. All maps are built with the resolution of 0.1 m and without any pre-processing of the input data.…”

Section: B Semantickitti Datasetmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping

Gan

Zhang

Grizzle

et al. 2020

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

This paper develops a Bayesian continuous 3D semantic occupancy map from noisy point cloud measurements. In particular, we generalize the Bayesian kernel inference model for occupancy (binary) map building to semantic (multi-class) maps. The method nicely reverts to the original occupancy mapping framework when only one occupied class exists in obtained measurements. First, using Categorical likelihood and its conjugate prior distribution, we extend the counting sensor model for binary classification to a multi-class classification problem which results in a unified probabilistic model for both occupancy and semantic probabilities. Secondly, by applying a Bayesian spatial kernel inference to the semantic counting sensor model, we relax the independent grid assumption and bring smoothness and continuity to the map inference. These latter properties enable the method to exploit local correlations present in the environment to predict semantic probabilities in regions unobserved by the sensor while increasing the performance. Lastly, computational efficiency and scalability are achieved by leveraging sparse kernels and a test-data octrees data structure. The evaluations using multiple sequences of stereo camera and LiDAR datasets show that the proposed method consistently outperforms the compared baselines. We also present a qualitative evaluation using data collected by a biped robot platform on the University of Michigan -North Campus.

show abstract

Section: B Semantickitti Datasetmentioning

confidence: 99%

Section: B Semantickitti Datasetmentioning

confidence: 99%

Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping

Gan

Zhang

Grizzle

et al. 2020

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

show abstract

“…Furthermore, we compare our approach to the state-of-theart semantic segmentation models RangeNet21, RangeNet53 [32] and LiLaNet [7], which provide comparable results. Consequently, we prove that the basic idea of CNN-based weather segmentation and de-noising is valuable and sur-passes geometrically based approaches.…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, this approach is able to also incorporate the intensity information of the point cloud. The semantic segmentation task is being further developed by a large scientific community and is already applied to the lidar point cloud domain, showing very promising results [30]- [32]. A major advantage is that the algorithms can generalize very well and thus recognize objects at different distances and orientations.…”

Section: Semantic Segmentation For Sparse Point Cloudsmentioning

confidence: 99%

CNN-Based Lidar Point Cloud De-Noising in Adverse Weather

Heinzler

Piewak

Schindler

et al. 2020

IEEE Robot. Autom. Lett.

150

View full text Add to dashboard Cite

Lidar sensors are frequently used in environment perception for autonomous vehicles and mobile robotics to complement camera, radar, and ultrasonic sensors. Adverse weather conditions are significantly impacting the performance of lidarbased scene understanding by causing undesired measurement points that in turn effect missing detections and false positives. In heavy rain or dense fog, water drops could be misinterpreted as objects in front of the vehicle which brings a mobile robot to a full stop.In this paper, we present the first CNN-based approach to understand and filter out such adverse weather effects in point cloud data. Using a large data set obtained in controlled weather environments, we demonstrate a significant performance improvement of our method over stateof-the-art involving geometric filtering. Data is available at https://github.com/rheinzler/PointCloudDeNoising.• The first CNN-based approach to lidar point cloud

show abstract

“…Though semantic segmentation was first introduced to process camera images, many methods have been proposed for segmenting LiDAR points as well (e.g. [51]- [56]). Many datasets have been published for semantic segmentation, such as Cityscape [57], KITTI [6], Toronto City [58], Mapillary Vistas [59], and ApolloScape [60].…”

Section: Deep Semantic Segmentationmentioning

confidence: 99%

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

Feng

Haase-Schütz

Rosenbaum

et al. 2021

IEEE Trans. Intell. Transport. Syst.

897

338

View full text Add to dashboard Cite

Recent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually equipped with different sensors (e.g. cameras, LiDARs, Radars), and multiple sensing modalities can be fused to exploit their complementary properties. In this context, many methods have been proposed for deep multi-modal perception problems. However, there is no general guideline for network architecture design, and questions of "what to fuse", "when to fuse", and "how to fuse" remain open. This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi-modal object detection and semantic segmentation in autonomous driving. To this end, we first provide an overview of on-board sensors on test vehicles, open datasets, and background information for object detection and semantic segmentation in autonomous driving research. We then summarize the fusion methodologies and discuss challenges and open questions. In the appendix, we provide tables that summarize topics and methods. We also provide an interactive online platform to navigate each reference: https://boschresearch.github.io/multimodalperception/. 0.99 0.8 0.98 0.99 0.96 0.96 0.94 Vehicle Person Road sign Traffic light LiDAR Points Map Radar Points RGB Image

show abstract

RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation

Cited by 959 publications

References 15 publications

Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping

Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping

CNN-Based Lidar Point Cloud De-Noising in Adverse Weather

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

Contact Info

Product

Resources

About