EasyOcc: 3D Pseudo-Label Supervision for Fully Self-Supervised Semantic Occupancy Prediction Models

• URL: http://arxiv.org/abs/2509.26087v2
• Date: Thu, 23 Oct 2025 08:37:14 GMT
• Title: EasyOcc: 3D Pseudo-Label Supervision for Fully Self-Supervised Semantic Occupancy Prediction Models
• Authors: Seamie Hayes, Ganesh Sistu, Ciarán Eising,
• Abstract summary: Self-supervised models have recently achieved notable advancements, particularly in the domain of semantic occupancy prediction.<n>These models utilize sophisticated loss computation strategies to compensate for the absence of ground-truth labels.<n>We propose 3D pseudo-ground-truth labels generated by the foundation models Grounded-SAM and Metric3Dv2, and harness temporal information for label densification.
• Score: 4.347156881555099
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised models have recently achieved notable advancements, particularly in the domain of semantic occupancy prediction. These models utilize sophisticated loss computation strategies to compensate for the absence of ground-truth labels. For instance, techniques such as novel view synthesis, cross-view rendering, and depth estimation have been explored to address the issue of semantic and depth ambiguity. However, such techniques typically incur high computational costs and memory usage during the training stage, especially in the case of novel view synthesis. To mitigate these issues, we propose 3D pseudo-ground-truth labels generated by the foundation models Grounded-SAM and Metric3Dv2, and harness temporal information for label densification. Our 3D pseudo-labels can be easily integrated into existing models, which yields substantial performance improvements, with mIoU increasing by 45\%, from 9.73 to 14.09, when implemented into the OccNeRF model. This stands in contrast to earlier advancements in the field, which are often not readily transferable to other architectures. Additionally, we propose a streamlined model, EasyOcc, achieving 13.86 mIoU. This model conducts learning solely from our labels, avoiding complex rendering strategies mentioned previously. Furthermore, our method enables models to attain state-of-the-art performance when evaluated on the full scene without applying the camera mask, with EasyOcc achieving 7.71 mIoU, outperforming the previous best model by 31\%. These findings highlight the critical importance of foundation models, temporal context, and the choice of loss computation space in self-supervised learning for comprehensive scene understanding.

Related papers

• Simplicity Prevails: The Emergence of Generalizable AIGI Detection in Visual Foundation Models [15.709482146201283]
  A simple linear classifier trained on the frozen features of modern Vision Foundation Models establishes a new state-of-the-art.<n>We show that this baseline matches specialized detectors on standard benchmarks but also decisively outperforms them on in-the-wild datasets.<n>We conclude by advocating for a paradigm shift in AI forensics, moving from overfitting on static benchmarks to harnessing the evolving world knowledge of foundation models for real-world reliability.
  arXiv  Detail & Related papers  (2026-02-02T07:20:02Z)
• Visual Autoregressive Modelling for Monocular Depth Estimation [69.01449528371916]
  We propose a monocular depth estimation method based on visual autoregressive ( VAR) priors.<n>Our method adapts a large-scale text-to-image VAR model and introduces a scale-wise conditional upsampling mechanism.<n>We report state-of-the-art performance in indoor benchmarks under constrained training conditions, and strong performance when applied to outdoor datasets.
  arXiv  Detail & Related papers  (2025-12-27T17:08:03Z)
• DINOv3 [62.31809406012177]
  Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures.<n>This technical report introduces DINOv3, a major milestone toward realizing this vision by leveraging simple yet effective strategies.<n>DINOv3 produces high-quality dense features that achieve outstanding performance on various vision tasks.
  arXiv  Detail & Related papers  (2025-08-13T18:00:55Z)
• Improving Object Detection by Modifying Synthetic Data with Explainable AI [3.0519884745675485]
  We propose a novel conceptual approach to improve the efficiency of designing synthetic images.<n>XAI techniques guide a human-in-the-loop process of modifying 3D mesh models used to generate these images.<n>We show that synthetic data can improve detection of vehicles in orientations unseen in training by 4.6%.
  arXiv  Detail & Related papers  (2024-12-02T13:24:43Z)
• Data-efficient Large Vision Models through Sequential Autoregression [58.26179273091461]
  We develop an efficient, autoregression-based vision model on a limited dataset. We demonstrate how this model achieves proficiency in a spectrum of visual tasks spanning both high-level and low-level semantic understanding. Our empirical evaluations underscore the model's agility in adapting to various tasks, heralding a significant reduction in the parameter footprint.
  arXiv  Detail & Related papers  (2024-02-07T13:41:53Z)
• FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language Models [59.13757801286343]
  Few-shot class-incremental learning aims to mitigate the catastrophic forgetting issue when a model is incrementally trained on limited data.<n>We introduce the FILP-3D framework with two novel components: the Redundant Feature Eliminator (RFE) for feature space misalignment and the Spatial Noise Compensator (SNC) for significant noise.
  arXiv  Detail & Related papers  (2023-12-28T14:52:07Z)
• Efficiently Robustify Pre-trained Models [18.392732966487582]
  robustness of large scale models towards real-world settings is still a less-explored topic. We first benchmark the performance of these models under different perturbations and datasets. We then discuss on how complete model fine-tuning based existing robustification schemes might not be a scalable option given very large scale networks.
  arXiv  Detail & Related papers  (2023-09-14T08:07:49Z)
• Retrieval-Enhanced Contrastive Vision-Text Models [61.783728119255365]
  We propose to equip vision-text models with the ability to refine their embedding with cross-modal retrieved information from a memory at inference time. Remarkably, we show that this can be done with a light-weight, single-layer, fusion transformer on top of a frozen CLIP. Our experiments validate that our retrieval-enhanced contrastive (RECO) training improves CLIP performance substantially on several challenging fine-grained tasks.
  arXiv  Detail & Related papers  (2023-06-12T15:52:02Z)
• Robust Category-Level 3D Pose Estimation from Synthetic Data [17.247607850702558]
  We introduce SyntheticP3D, a new synthetic dataset for object pose estimation generated from CAD models. We propose a novel approach (CC3D) for training neural mesh models that perform pose estimation via inverse rendering.
  arXiv  Detail & Related papers  (2023-05-25T14:56:03Z)
• Studying How to Efficiently and Effectively Guide Models with Explanations [52.498055901649025]
  'Model guidance' is the idea of regularizing the models' explanations to ensure that they are "right for the right reasons" We conduct an in-depth evaluation across various loss functions, attribution methods, models, and 'guidance depths' on the PASCAL VOC 2007 and MS COCO 2014 datasets. Specifically, we guide the models via bounding box annotations, which are much cheaper to obtain than the commonly used segmentation masks.
  arXiv  Detail & Related papers  (2023-03-21T15:34:50Z)
• Imposing Consistency for Optical Flow Estimation [73.53204596544472]
  Imposing consistency through proxy tasks has been shown to enhance data-driven learning. This paper introduces novel and effective consistency strategies for optical flow estimation.
  arXiv  Detail & Related papers  (2022-04-14T22:58:30Z)
• Secrets of 3D Implicit Object Shape Reconstruction in the Wild [92.5554695397653]
  Reconstructing high-fidelity 3D objects from sparse, partial observation is crucial for various applications in computer vision, robotics, and graphics. Recent neural implicit modeling methods show promising results on synthetic or dense datasets. But, they perform poorly on real-world data that is sparse and noisy. This paper analyzes the root cause of such deficient performance of a popular neural implicit model.
  arXiv  Detail & Related papers  (2021-01-18T03:24:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.