Flux-GS is a mobile-optimized 3D Gaussian Splatting method that compresses specular energy via Monte Carlo aggregation, recovers details with attribute-conditioned SH offsets, and uses multi-view guidance for densification to cut parameters while keeping visual quality.
3drealcar: An in-the-wild rgb-d car dataset with 360-degree views
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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HiFiVe is a training-free framework using an auto-regressive texture refinement pipeline with depth-based warping, multi-view fusion, and symmetry to enhance both texture and geometry fidelity in vehicle generation from 2D priors.
A new framework generates part-level animatable 3D Gaussian vehicles from images by adding modules for exclusive part ownership and kinematic joint/axis prediction.
MM-TRELLIS extends TRELLIS with LiDAR point-cloud guidance and multi-view image conditioning plus voxel filtering to generate high-fidelity 3D vehicle meshes from in-the-wild driving data.
REAP trains an end-to-end SAC policy with behavior cloning and collision penalties inside a 3DGS Real2Sim simulator and transfers it to physical vehicles, succeeding in narrow mechanical parking slots.
3DCarGen synthesizes 3D-consistent multi-view images from one input photo, builds a coarse 3D Gaussian representation, then generates arbitrary views and recovers detailed meshes with color-normal optimization for real-world car images.
citing papers explorer
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Monte Carlo Energy Aggregation for Mobile 3D Gaussian Splatting
Flux-GS is a mobile-optimized 3D Gaussian Splatting method that compresses specular energy via Monte Carlo aggregation, recovers details with attribute-conditioned SH offsets, and uses multi-view guidance for densification to cut parameters while keeping visual quality.
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HiFiVe: High-Fidelity Vehicle Generation Leveraging Auto-Regressive 2D Generative Priors
HiFiVe is a training-free framework using an auto-regressive texture refinement pipeline with depth-based warping, multi-view fusion, and symmetry to enhance both texture and geometry fidelity in vehicle generation from 2D priors.
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Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation
A new framework generates part-level animatable 3D Gaussian vehicles from images by adding modules for exclusive part ownership and kinematic joint/axis prediction.
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MM-TRELLIS: Point-Cloud Guided Multi-Modal 3D Vehicle Generation in Autonomous Driving
MM-TRELLIS extends TRELLIS with LiDAR point-cloud guidance and multi-view image conditioning plus voxel filtering to generate high-fidelity 3D vehicle meshes from in-the-wild driving data.
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REAP: Reinforcement-Learning End-to-End Autonomous Parking with Gaussian Splatting Simulator for Real2Sim2Real Transfer
REAP trains an end-to-end SAC policy with behavior cloning and collision penalties inside a 3DGS Real2Sim simulator and transfers it to physical vehicles, succeeding in narrow mechanical parking slots.
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3DCarGen: Scalable 3D Car Generation via 3D-consistent Multi-view Synthesis
3DCarGen synthesizes 3D-consistent multi-view images from one input photo, builds a coarse 3D Gaussian representation, then generates arbitrary views and recovers detailed meshes with color-normal optimization for real-world car images.