arXiv Computer Vision
BikeScenes: LiDAR Semantic Segmentation for Bicycles
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Sep 10
CLFTv2: Efficient Camera-LiDAR Fusion for Semantic Segmentation via Hierarchical Feature Pyramids
arXiv:2609.09881v1 Announce Type: new Abstract: Semantic segmentation for autonomous driving requires reliable detection of vulnerable road users (VRUs) despite heavy class imbalance. We introduce CL...
arXiv Computer Vision
Oct 26
IGLOSS: Image Generation for Lidar Open-vocabulary Semantic Segmentation
IGLOSS introduces a zero‑shot open‑vocabulary semantic segmentation approach for 3D automotive LiDAR data. The method generates prototype images from text to bridge the image‑text modality gap, then matches 3D point features with 2D image features derived from a Vision Foundation Model. It achieves state‑of‑the‑art results on nuScenes and SemanticKITTI, with code, models, and images publicly available.
arXiv Computer Vision
Oct 26
DriveOSS: Dataset and Benchmark for 3D Anomaly Segmentation and Open-set Semantic Segmentation in Autonomous Driving
DriveOSS is a new real‑world multimodal dataset and benchmark for 3D anomaly segmentation and open‑set semantic segmentation in autonomous driving. It contains 12,661 synchronized frames from six RGB cameras, a 128‑beam LiDAR, and GPS/IMU, covering 52 driving scenes under various weather conditions. The data are split into in‑distribution (ID) scenes for training and out‑of‑distribution (OOD) scenes for validation and testing, with unknown objects labeled as an outlier class to challenge current 3D perception systems.
arXiv Machine Learning
Sep 14
A Multi-Vehicle Dataset with Camera, LiDAR, and Radar Sensors and Scanned 3D Models for Custom Auto-Annotation using RTK-GNSS
The paper introduces a multi-vehicle dataset that includes camera, LiDAR, and radar sensor data along with scanned 3D models of all vehicles. Each vehicle’s pose and continuous kinematics are provided via RTK‑GNSS, enabling precise knowledge of the dynamic surroundings at any time. The dataset supports single‑ and multi‑object recordings with seven target vehicles, allowing evaluation of measurement effects such as occlusion and reflections thanks to known vehicle surface normals.
arXiv Computer Vision
Oct 27
VaViT: Vanilla ViT for Automotive Point Cloud Semantic Segmentation
arXiv:2605.31177v2 Announce Type: replace Abstract: Plain Transformers have become the de-facto architecture for processing text, audio, image, and video, offering a unified backbone for multimodal l...
arXiv Computer Vision
2d ago
Edge-Assisted Crowdsensing for Explainable Cyclist Safety Perception
The paper introduces an edge‑assisted crowdsensing framework that uses handlebar‑mounted cameras to capture street‑level video, a vision‑language model to extract structured traffic attributes, and an explainable boosting machine to combine these with OpenStreetMap data and on‑bike kinematics for a 1‑9 safety score. On 1,362 annotated frames from two London routes, the model achieves 72.2 % binary accuracy and a mean absolute error of 1.011, with feature attribution revealing the impact of lane separation, painted lanes, and construction sites on safety ratings. The system operates efficiently at the network edge, requiring only 2.49 s per frame for extraction and 0.4 ms for scoring, and supports continuous monitoring at a cost of 1.4 accuracy points for 1,000 riders over a 20‑second interval.