VistaBot integrates 4D geometry estimation and spatiotemporal view synthesis into action policies to improve cross-view generalization by 2.6-2.8x on a new VGS metric in simulation and real tasks.
Nerf: Representing scenes as neural radiance fields for view synthesis
9 Pith papers cite this work. Polarity classification is still indexing.
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VBGS-SLAM uses variational inference on conjugate Gaussian properties to couple 3DGS map refinement and pose tracking with closed-form updates and posterior uncertainty, reducing drift compared to deterministic methods.
CARLA-GS is a modular pipeline that uses an LLM for semantic trajectory planning, CARLA for physics execution, and 3D Gaussian Splatting for photorealistic rendering to synthesize autonomous driving corner cases.
GaussianMap learns adaptive Gaussian primitives on the BEV plane from multi-sensor data to produce vectorized HD maps, reporting state-of-the-art results on nuScenes and Argoverse 2.
Real2Sim reconstructs editable dynamic driving scenes as temporally continuous Gaussians integrated with a differentiable MPM physics solver for high-fidelity simulation of interactions and collisions.
NavCrafter generates controllable novel-view videos from one image via video diffusion, geometry-aware expansion, and enhanced 3D Gaussian Splatting to achieve state-of-the-art synthesis under large viewpoint changes.
Splatblox creates a traversability-aware ESDF from RGB-LiDAR fusion via Gaussian Splatting, enabling semantic navigation that outperforms prior methods by over 50% success rate in vegetated field trials on quadruped and wheeled robots.
A hybrid structural latent points representation is learned by inserting a point-wise latent VAE into a point-cloud autoencoder and regularizing toward a Gaussian prior, paired with a lightweight 3DGS rendering pipeline, yielding gains on RLBench and ManiSkill2 benchmarks.
LSGS-Loc delivers state-of-the-art accuracy and robustness for 3DGS-based visual localization in large UAV scenes via scale-aware initialization and reliability masking without scene-specific training.
citing papers explorer
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VistaBot: View-Robust Robot Manipulation via Spatiotemporal-Aware View Synthesis
VistaBot integrates 4D geometry estimation and spatiotemporal view synthesis into action policies to improve cross-view generalization by 2.6-2.8x on a new VGS metric in simulation and real tasks.
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VBGS-SLAM: Variational Bayesian Gaussian Splatting Simultaneous Localization and Mapping
VBGS-SLAM uses variational inference on conjugate Gaussian properties to couple 3DGS map refinement and pose tracking with closed-form updates and posterior uncertainty, reducing drift compared to deterministic methods.
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CARLA-GS: Decoupling Representation, Reasoning, and Physics Simulation for Autonomous Driving Corner-Case Synthesis
CARLA-GS is a modular pipeline that uses an LLM for semantic trajectory planning, CARLA for physics execution, and 3D Gaussian Splatting for photorealistic rendering to synthesize autonomous driving corner cases.
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GaussianMap: Learning Gaussian Representation for Multi-Sensor Online HD Map Construction
GaussianMap learns adaptive Gaussian primitives on the BEV plane from multi-sensor data to produce vectorized HD maps, reporting state-of-the-art results on nuScenes and Argoverse 2.
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Real2Sim: A Physics-driven and Editable Gaussian Splatting Framework for Autonomous Driving Scenes
Real2Sim reconstructs editable dynamic driving scenes as temporally continuous Gaussians integrated with a differentiable MPM physics solver for high-fidelity simulation of interactions and collisions.
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NavCrafter: Exploring 3D Scenes from a Single Image
NavCrafter generates controllable novel-view videos from one image via video diffusion, geometry-aware expansion, and enhanced 3D Gaussian Splatting to achieve state-of-the-art synthesis under large viewpoint changes.
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Splatblox: Traversability-Aware Gaussian Splatting for Outdoor Robot Navigation
Splatblox creates a traversability-aware ESDF from RGB-LiDAR fusion via Gaussian Splatting, enabling semantic navigation that outperforms prior methods by over 50% success rate in vegetated field trials on quadruped and wheeled robots.
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Learning Structural Latent Points for Efficient Visual Representations in Robotic Manipulation
A hybrid structural latent points representation is learned by inserting a point-wise latent VAE into a point-cloud autoencoder and regularizing toward a Gaussian prior, paired with a lightweight 3DGS rendering pipeline, yielding gains on RLBench and ManiSkill2 benchmarks.
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LSGS-Loc: Towards Robust 3DGS-Based Visual Localization for Large-Scale UAV Scenarios
LSGS-Loc delivers state-of-the-art accuracy and robustness for 3DGS-based visual localization in large UAV scenes via scale-aware initialization and reliability masking without scene-specific training.