mlsanityData to 14 Oct 2026

RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for Real-world Applications

arXiv 2404.03962 ↗First posted 5 Apr 2024cs.CVcited by 5

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Abstract (arXiv)

In robotic vision, a de-facto paradigm is to learn in simulated environments and then transfer to real-world applications, which poses an essential challenge in bridging the sim-to-real domain gap. While mainstream works tackle this problem in the RGB domain, we focus on depth data synthesis and develop a range-aware RGB-D data simulation pipeline (RaSim). In particular, high-fidelity depth data is generated by imitating the imaging principle of real-world sensors. A range-aware rendering strategy is further introduced to enrich data diversity. Extensive experiments show that models trained with RaSim can be directly applied to real-world scenarios without any finetuning and excel at downstream RGB-D perception tasks.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 54 of its 64 references that are in the map. First posted on arXiv 5 Apr 2024.

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54 of 64 references.

References · 54

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38 more
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50 most cited of 54 All 54 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 2.

5 papers.

  1. 2024UNOPose: Unseen Object Pose Estimation with an Unposed RGB-D Reference Imagecited by 12
  2. 2024Street Gaussians without 3D Object Trackercited by 4
  3. 2026DAWN: Noise-Robust Quadruped Parkour via Depth-Denoising World Modelscited by 1
  4. 2025Vanishing Depth: Training Generalized Depth Adapters with Sinusoidal Depth Preprocessing for Pretrained RGB Encoderscited by 0
  5. 2026GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generationcited by 0