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Object-centric Reconstruction and Tracking of Dynamic Unknown Objects using 3D Gaussian Splatting
Robotics · Submitted 30 May 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The experiments demonstrate successful 3D reconstruction and accurate 6-DoF tracking of the target object in proximity operations over a short to medium duration.
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The abstract, sentence by sentence
Generalizable perception is one of the pillars of high-level autonomy in space robotics. Estimating the structure and motion of unknown objects in dynamic environments is fundamental for such autonomous systems. Traditionally, the solutions have relied on prior knowledge of target objects, multiple disparate representations, or low-fidelity outputs unsuitable for robotic operations. This work proposes a novel approach to incrementally reconstruct and track a dynamic unknown object using a unified representation -- a set of 3D Gaussian blobs that describe its geometry and appearance. The differentiable 3D Gaussian Splatting framework is adapted to a dynamic object-centric setting. The input to the pipeline is a sequential set of RGB-D images. 3D reconstruction and 6-DoF pose tracking tasks are tackled using first-order gradient-based optimization. The formulation is simple, requires no pre-training, assumes no prior knowledge of the object or its motion, and is suitable for online applications. The proposed approach is validated on a dataset of 10 unknown spacecraft of diverse geometry and texture under arbitrary relative motion. The experiments demonstrate successful 3D reconstruction and accurate 6-DoF tracking of the target object in proximity operations over a short to medium duration. The causes of tracking drift are discussed and potential solutions are outlined.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Generalizable perception is one of the pillars of high-level autonomy in space robotics. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | Estimating the structure and motion of unknown objects in dynamic environments is fundamental for such autonomous systems. | background 1.00 | 0.04 | 0.03 | asserted 1.00 |
| s2 | Traditionally, the solutions have relied on prior knowledge of target objects, multiple disparate representations, or low-fidelity outputs unsuitable for robotic operations. | gap 0.69 | 0.05 | 0.03 | asserted 1.00 |
| s3 | This work proposes a novel approach to incrementally reconstruct and track a dynamic unknown object using a unified representation -- a set of 3D Gaussian blobs that describe its geometry and appearance. | contribution 0.98 | 0.87 | 0.24 | asserted 0.99 |
| s4 | The differentiable 3D Gaussian Splatting framework is adapted to a dynamic object-centric setting. | method 0.82 | 0.84 | 0.05 | asserted 1.00 |
| s5 | The input to the pipeline is a sequential set of RGB-D images. 3D reconstruction and 6-DoF pose tracking tasks are tackled using first-order gradient-based optimization. | method 1.00 | 0.80 | 0.04 | asserted 1.00 |
| s6 | The formulation is simple, requires no pre-training, assumes no prior knowledge of the object or its motion, and is suitable for online applications. | method 0.53 | 0.66 | 0.10 | asserted 1.00 |
| s7 | The proposed approach is validated on a dataset of 10 unknown spacecraft of diverse geometry and texture under arbitrary relative motion. | method 0.72 | 0.95 | 0.10 | asserted 0.99 |
| s8 | The experiments demonstrate successful 3D reconstruction and accurate 6-DoF tracking of the target object in proximity operations over a short to medium duration. | result 1.00 | 0.96 | 0.73 | asserted 1.00 |
| s9 | The causes of tracking drift are discussed and potential solutions are outlined. | limitation 0.53 | 0.74 | 0.04 | asserted 0.67 |
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The paper
Original abstract
Generalizable perception is one of the pillars of high-level autonomy in space robotics. Estimating the structure and motion of unknown objects in dynamic environments is fundamental for such autonomous systems. Traditionally, the solutions have relied on prior knowledge of target objects, multiple disparate representations, or low-fidelity outputs unsuitable for robotic operations. This work proposes a novel approach to incrementally reconstruct and track a dynamic unknown object using a unified representation -- a set of 3D Gaussian blobs that describe its geometry and appearance. The differentiable 3D Gaussian Splatting framework is adapted to a dynamic object-centric setting. The input to the pipeline is a sequential set of RGB-D images. 3D reconstruction and 6-DoF pose tracking tasks are tackled using first-order gradient-based optimization. The formulation is simple, requires no pre-training, assumes no prior knowledge of the object or its motion, and is suitable for online applications. The proposed approach is validated on a dataset of 10 unknown spacecraft of diverse geometry and texture under arbitrary relative motion. The experiments demonstrate successful 3D reconstruction and accurate 6-DoF tracking of the target object in proximity operations over a short to medium duration. The causes of tracking drift are discussed and potential solutions are outlined.
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References
How the literature uses it
2 arXiv papers cite this in 2 sentences
Papers citing it · 1 sentence without a confident label
Uses its method or tool 1 paper
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These models can be either reconstructed [ref] from videos or retrieved from the 3D mesh library.
FMimic: Foundation Models are Fine-grained Action Learners from Human Videos · Interaction estimation · 0.81
What it cites · 5 sentences without a confident label
Uses the method or tool of 1 paper
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We use Adam [ref] to optimize the Gaussian and pose parameters.
Adam: A Method for Stochastic Optimization · Reconstruction and Tracking · 1.00
Cites as background 1 paper
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Many state-of-the-art approaches require a 3D model of the object as a template [ref] .
MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare · Introduction · 0.89
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