The paper proposes a three-way open-set detection framework for autonomous navigation, classifying each detection as a known object, unknown object, or background based on a pretrained detector’s outputs. It introduces domain generalization and adaptation methods, evaluates them across various detector families and benchmarks, and demonstrates that this approach improves safety and efficiency in simulated navigation missions compared to binary detection methods.
arXiv:2609.22896v1 Announce Type: new Abstract: Autonomous vehicles operating in open-world scenarios are inevitably confronted with previously unknown objects, such as exotic animals or loose cargo....
The paper introduces Out-of-Distribution Semantic Occupancy Prediction, a task that focuses on detecting unknown objects in 3D voxel space for autonomous driving. It proposes Realistic Anomaly Augmentation to create two new datasets, VAA-KITTI and VAA-KITTI-360, and presents the OccOoD framework, which uses Cross‑Space Semantic Refinement to improve OoD detection while maintaining semantic occupancy accuracy. Experiments show OccOoD achieves an AuROC of 65.50% and an AuPRCr of 31.83% within a 1.2 m radius, demonstrating strong generalization to real‑world urban scenes.
The paper introduces an open‑vocabulary 3D object detection pipeline that uses a promptable segmentation model (SAM3) to generate instance masks from six surround‑view cameras. These masks are converted into metric 3D boxes, achieving up to 0.413 mAP/0.555 NDS without any training when supervised box geometry is borrowed at inference. The approach also improves a supervised LiDAR‑only detector by 0.034 mAP through a camera‑witness rule, demonstrating that measurement precision, not 2D detection, limits performance.
arXiv:2608. 19973v1 Announce Type: cross Abstract: Recently, open-vocabulary 3D object detection (3D-OVD) has gained increasing attention for its ability to detect unseen objects in 3D scenes.
The paper introduces StopWatch, a method that explicitly grounds the goal in object-goal navigation tasks. By addressing the dominant failure of goal grounding in fine‑tuned vision‑language models, StopWatch achieves state‑of‑the‑art success rates on six benchmarks across three scene collections and corrects annotation defects in these datasets. The authors demonstrate that with accurate goal grounding, their policy nearly matches the expert demonstrations, highlighting perception as the remaining bottleneck.
arXiv:2610.35374v1 Announce Type: new Abstract: Single-Domain Generalized Object Detection (S-DGOD) aims to generalize from a single source domain to unseen domains under distribution shifts. Existin...
The paper proposes a modular training pipeline for zero‑shot cross‑city object detection that combines a multi‑dataset pre‑training strategy with class‑agnostic objectness distillation and a domain‑resilient augmentation stream featuring a Grayworld transformation. Applied to the RF‑DETR detector, the approach reduces cross‑city distribution gaps while using only 16 GB GPU memory, achieving a 24.29‑point mAP improvement and 1st place on the AI City Challenge Track 6 leaderboard. The authors provide code and data at the referenced GitHub repository.
arXiv:2610.19330v1 Announce Type: new Abstract: Reliable recognition of accessibility-critical objects (e.g., audible pedestrian signals, door-activation buttons, and handrails) is essential for assi...
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.
Reliable recognition of accessibility-critical objects (e.g., audible pedestrian signals, door-activation buttons, and handrails) is essential for assistive navigation technologies that support safe,...
arXiv:2608. 07577v1 Announce Type: cross Abstract: A closed-set detector for autonomous driving must assign every object one of a fixed set of labels.
The paper introduces Structured Prior Knowledge (SPK), a framework that extracts and organizes latent priors from pretrained object detectors to improve out-of-distribution (OoD) detection. SPK uses in-distribution data and hallucination-inducing samples to elicit part-level semantic concepts, then combines these with geometric and contextual priors into a compact five-dimensional representation. Experiments across various detector architectures and OoD benchmarks show that SPK achieves state-of-the-art performance, demonstrating that pretrained detectors encode richer latent knowledge than previously exploited.