arXiv subjects

Andrew Caunes

Publications and source records attributed to Andrew Caunes.

3 recordsLinked to original sources

arXiv2026

FreeOcc: Training-free Panoptic Occupancy Prediction via Foundation Models

Semantic and panoptic occupancy prediction for road scene analysis provides a dense 3D representation of the ego vehicle's surroundings. Current camera-only approaches typically rely on costly dense 3D supervision or require training models on data from the target domain, limiting deployment in unseen environments. We propose FreeOcc, a training-free pipeline that leverages pretrained foundation models to recover both semantics and geometry from multi-view images. FreeOcc extracts per-view panoptic priors with a promptable foundation segmentation model and prompt-to-taxonomy rules, and reconstructs metric 3D points with a reconstruction foundation model. Depth- and confidence- aware filtering lifts reliable labels into 3D, which are fused over time and voxelized with a deterministic refinement stack. For panoptic occupancy, instances are recovered by fitting and merging robust current-view 3D box candidates, enabling instance-aware occupancy without any learned 3D model. On Occ3D-nuScenes, FreeOcc achieves 16.9 mIoU and 16.5 RayIoU train-free, on par with state-of-the-art weakly supervised methods. When employed as a pseudo-label generation pipeline for training downstream models, it achieves 21.1 RayIoU, surpassing the previous state-of-the-art weakly supervised baseline. Furthermore, FreeOcc sets new baselines for both train-free and weakly supervised panoptic occupancy prediction, achieving 3.1 RayPQ and 3.9 RayPQ, respectively. These results highlight foundation-model-driven perception as a practical route to training-free 3D scene understanding.

cs.CV↗

arXiv2026

Multi-View Projection for Unsupervised Domain Adaptation in 3D Semantic Segmentation

3D semantic segmentation plays a pivotal role in autonomous driving and road infrastructure analysis, yet state-of-the-art 3D models are prone to severe domain shift when deployed across different datasets. In this paper, we propose an Unsupervised Domain Adaptation approach where a 3D segmentation model is trained on the target dataset using pseudo-labels generated by a novel multi-view projection framework. Our approach first aligns Lidar scans into coherent 3D scenes and renders them from multiple virtual camera poses to create large-scale synthetic 2D semantic segmentation datasets in various modalities. The generated datasets are used to train an ensemble of 2D segmentation models in point cloud view domain on each modality. During inference, the models process a large amount of views per scene; the resulting logits are back-projected to 3D with a depth-aware voting scheme to generate final point-wise labels. These labels are then used to fine-tune a 3D segmentation model in the target domain. We evaluate our approach Real-to-Real on the nuScenes and SemanticKITTI datasets. We also evaluate it Simulation-to-Real with the SynLidar dataset. Our contributions are a novel method that achieves state-of-the-art results in Real-to-Real Unsupervised Domain Adaptation, and we also demonstrate an application of our method to segment rare classes, for which target 3D annotations are not available, by only using 2D annotations for those classes and leveraging 3D annotations for other classes in a source domain.

cs.CV↗

arXiv2025

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation

Semantic segmentation of 3D LiDAR point clouds, essential for autonomous driving and infrastructure management, is best achieved by supervised learning, which demands extensive annotated datasets and faces the problem of domain shifts. We introduce a new 3D semantic segmentation pipeline that leverages aligned scenes and state-of-the-art 2D segmentation methods, avoiding the need for direct 3D annotation or reliance on additional modalities such as camera images at inference time. Our approach generates 2D views from LiDAR scans colored by sensor intensity and applies 2D semantic segmentation to these views using a camera-domain pretrained model. The segmented 2D outputs are then back-projected onto the 3D points, with a simple voting-based estimator that merges the labels associated to each 3D point. Our main contribution is a global pipeline for 3D semantic segmentation requiring no prior 3D annotation and not other modality for inference, which can be used for pseudo-label generation. We conduct a thorough ablation study and demonstrate the potential of the generated pseudo-labels for the Unsupervised Domain Adaptation task.

cs.CV↗