Computer vision training dataset generation for robotic environments using Gaussian splatting

• URL: http://arxiv.org/abs/2512.13411v1
• Date: Mon, 15 Dec 2025 15:00:17 GMT
• Title: Computer vision training dataset generation for robotic environments using Gaussian splatting
• Authors: Patryk Niżeniec, Marcin Iwanowski,
• Abstract summary: This paper introduces a novel pipeline for generating large-scale, highly realistic, and automatically labeled datasets for computer vision tasks in robotic environments.<n>We leverage 3D Gaussian Splatting (3DGS) to create photorealistic representations of the operational environment and objects.<n>A novel, two-pass rendering technique combines the realism of splats with a shadow map generated from proxy meshes.<n> Pixel-perfect segmentation masks are generated automatically and formatted for direct use with object detection models like YOLO.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces a novel pipeline for generating large-scale, highly realistic, and automatically labeled datasets for computer vision tasks in robotic environments. Our approach addresses the critical challenges of the domain gap between synthetic and real-world imagery and the time-consuming bottleneck of manual annotation. We leverage 3D Gaussian Splatting (3DGS) to create photorealistic representations of the operational environment and objects. These assets are then used in a game engine where physics simulations create natural arrangements. A novel, two-pass rendering technique combines the realism of splats with a shadow map generated from proxy meshes. This map is then algorithmically composited with the image to add both physically plausible shadows and subtle highlights, significantly enhancing realism. Pixel-perfect segmentation masks are generated automatically and formatted for direct use with object detection models like YOLO. Our experiments show that a hybrid training strategy, combining a small set of real images with a large volume of our synthetic data, yields the best detection and segmentation performance, confirming this as an optimal strategy for efficiently achieving robust and accurate models.

Related papers

• Mirage2Matter: A Physically Grounded Gaussian World Model from Video [87.9732484393686]
  We present Simulate Anything, a graphics-driven world modeling and simulation framework.<n>Our approach reconstructs real-world environments into a photorealistic scene representation using 3D Gaussian Splatting (3DGS)<n>We then leverage generative models to recover a physically realistic representation and integrate it into a simulation environment via a precision calibration target.
  arXiv  Detail & Related papers  (2026-01-24T07:43:57Z)
• Optimizing Grasping in Legged Robots: A Deep Learning Approach to Loco-Manipulation [0.6533458718563319]
  This paper presents a framework designed to enhance the grasping capabilities of quadrupeds equipped with arms.<n>We developed a pipeline within the Genesis simulation environment to generate a synthetic dataset of grasp attempts on common objects.<n>This dataset was used to train a custom CNN with a U-Net-like architecture that processes multi-modal input from an onboard RGB and depth cameras.<n>We validated the complete framework on a four-legged robot.
  arXiv  Detail & Related papers  (2025-08-24T17:47:56Z)
• R3D2: Realistic 3D Asset Insertion via Diffusion for Autonomous Driving Simulation [78.26308457952636]
  This paper introduces R3D2, a lightweight, one-step diffusion model designed to overcome limitations in autonomous driving simulation.<n>It enables realistic insertion of complete 3D assets into existing scenes by generating plausible rendering effects-such as shadows and consistent lighting-in real time.<n>We show that R3D2 significantly enhances the realism of inserted assets, enabling use-cases like text-to-3D asset insertion and cross-scene/dataset object transfer.
  arXiv  Detail & Related papers  (2025-06-09T14:50:19Z)
• Synthetic Dataset Generation for Autonomous Mobile Robots Using 3D Gaussian Splatting for Vision Training [0.708987965338602]
  We propose a novel method for automatically generating annotated synthetic data in Unreal Engine.<n>We demonstrate that synthetic datasets can achieve performance comparable to that of real-world datasets.<n>This is the first application of synthetic data for training object detection algorithms in robot soccer.
  arXiv  Detail & Related papers  (2025-06-05T14:37:40Z)
• Cut-and-Splat: Leveraging Gaussian Splatting for Synthetic Data Generation [0.7864304771129751]
  We develop a synthetic data pipeline for generating context-aware instance segmentation training data for specific objects.<n>We train a Gaussian Splatting model of the target object and automatically extract the object from the video.<n>We then render the object on a random background image, and monocular depth estimation is employed to place the object in a believable pose.
  arXiv  Detail & Related papers  (2025-04-11T12:04:49Z)
• Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems [80.62854148838359]
  Eye image segmentation is a critical step in eye tracking that has great influence over the final gaze estimate. We use dimensionality-reduction techniques to measure the overlap between the target eye images and synthetic training data. Our methods result in robust, improved performance when tackling the discrepancy between simulation and real-world data samples.
  arXiv  Detail & Related papers  (2024-03-23T22:32:06Z)
• GeoSim: Photorealistic Image Simulation with Geometry-Aware Composition [81.24107630746508]
  We present GeoSim, a geometry-aware image composition process that synthesizes novel urban driving scenes. We first build a diverse bank of 3D objects with both realistic geometry and appearance from sensor data. The resulting synthetic images are photorealistic, traffic-aware, and geometrically consistent, allowing image simulation to scale to complex use cases.
  arXiv  Detail & Related papers  (2021-01-16T23:00:33Z)
• Photorealism in Driving Simulations: Blending Generative Adversarial Image Synthesis with Rendering [0.0]
  We introduce a hybrid generative neural graphics pipeline for improving the visual fidelity of driving simulations. We form 2D semantic images from 3D scenery consisting of simple object models without textures. These semantic images are then converted into photorealistic RGB images with a state-of-the-art Generative Adrial Network (GAN) trained on real-world driving scenes.
  arXiv  Detail & Related papers  (2020-07-31T03:25:17Z)
• Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition [67.9464567157846]
  We propose an autoencoder for joint generation of realistic images from synthetic 3D models while simultaneously decomposing real images into their intrinsic shape and appearance properties. Our experiments confirm that a joint treatment of rendering and decomposition is indeed beneficial and that our approach outperforms state-of-the-art image-to-image translation baselines both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2020-06-29T12:53:58Z)
• Two-shot Spatially-varying BRDF and Shape Estimation [89.29020624201708]
  We propose a novel deep learning architecture with a stage-wise estimation of shape and SVBRDF. We create a large-scale synthetic training dataset with domain-randomized geometry and realistic materials. Experiments on both synthetic and real-world datasets show that our network trained on a synthetic dataset can generalize well to real-world images.
  arXiv  Detail & Related papers  (2020-04-01T12:56:13Z)
• Deep CG2Real: Synthetic-to-Real Translation via Image Disentanglement [78.58603635621591]
  Training an unpaired synthetic-to-real translation network in image space is severely under-constrained. We propose a semi-supervised approach that operates on the disentangled shading and albedo layers of the image. Our two-stage pipeline first learns to predict accurate shading in a supervised fashion using physically-based renderings as targets.
  arXiv  Detail & Related papers  (2020-03-27T21:45:41Z)

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.