mlsanityData to 14 Oct 2026

NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization

arXiv 2305.17763 ↗First posted 28 May 2023cs.CVcited by 13

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Abstract (arXiv)

Monocular 3D object localization in driving scenes is a crucial task, but challenging due to its ill-posed nature. Estimating 3D coordinates for each pixel on the object surface holds great potential as it provides dense 2D-3D geometric constraints for the underlying PnP problem. However, high-quality ground truth supervision is not available in driving scenes due to sparsity and various artifacts of Lidar data, as well as the practical infeasibility of collecting per-instance CAD models. In this work, we present NeurOCS, a framework that uses instance masks and 3D boxes as input to learn 3D object shapes by means of differentiable rendering, which further serves as supervision for learning dense object coordinates. Our approach rests on insights in learning a category-level shape prior directly from real driving scenes, while properly handling single-view ambiguities. Furthermore, we study and make critical design choices to learn object coordinates more effectively from an object-centric view. Altogether, our framework leads to new state-of-the-art in monocular 3D localization that ranks 1st on the KITTI-Object benchmark among published monocular methods.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 73 of its 91 references that are in the map. First posted on arXiv 28 May 2023.

Builds on

73 of 91 references.

References · 73

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38 more
  1. 2018Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Drivingcited by 457
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50 most cited of 73 All 73 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 5; 2025: 4; 2026: 1.

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  1. 2024Deepfake Generation and Detection: A Benchmark and Surveycited by 67
  2. 2023Text2Loc: 3D Point Cloud Localization from Natural Languagecited by 35
  3. 2023Taming Self-Training for Open-Vocabulary Object Detectioncited by 25
  4. 2024A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trendscited by 16
  5. 2024SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objectscited by 12
  6. 2023Generating Enhanced Negatives for Training Language-Based Object Detectorscited by 11
  7. 2024Semantically-aware Neural Radiance Fields for Visual Scene Understanding: A Comprehensive Reviewcited by 9
  8. 2025CHARM3R: Towards Unseen Camera Height Robust Monocular 3D Detectorcited by 6
  9. 2026Towards Intrinsic-Aware Monocular 3D Object Detectioncited by 4
  10. 2024Instantaneous Perception of Moving Objects in 3Dcited by 3
  11. 2025MAESTRO: Task-Relevant Optimization via Adaptive Feature Enhancement and Suppression for Multi-task 3D Perceptioncited by 0
  12. 2025Densemarks: Learning Canonical Embeddings for Human Heads Images via Point Trackscited by 0
1 more
  1. 2025Text2Loc++: Generalizing 3D Point Cloud Localization from Natural Languagecited by 0