Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation
Authors: Shiyao Qian, Yuan Ren, Dongfeng Bai, Bingbing Liu
Organizations: The authors are with Huawei Noah’s Ark Lab. · Shiyao Qian is with the University of Toronto and contributed to this work during an internship at Huawei Noah’s Ark Lab, Canada.
Simulation is essential for autonomous driving, yet current frameworks often model vehicles as rigid assets and fail to capture part-level articulation. With perception algorithms increasingly leveraging dynamics such as wheel steering or door opening, realistic simulation requires animatable vehicle representations. Existing CAD-based pipelines are limited by library coverage and fixed templates, preventing faithful reconstruction of in-the-wild instances. We propose a generative framework that, from a single image or sparse multi-view input, synthesizes an animatable 3D Gaussian vehicle. Our method addresses two challenges: (i) large 3D asset generators are optimized for static quality but not articulation, leading to distortions at part boundaries when animated; and (ii) segmentation alone cannot provide the kinematic parameters required for motion. To overcome this, we introduce a part-edge refinement module that enforces exclusive Gaussian ownership and a kinematic reasoning head that predicts joint positions and hinge axes of movable parts. Together, these components enable faithful part-aware simulation, bridging the gap between static generation and animatable vehicle models.
Figures & tables
Fig. 1: Overview of the PointNet++ hierarchical feature learning architecture. For segmentation, learned features are interpolated back to the original point set to produce per-point scores. For classification, an additional abstraction layer aggregates features into a global representation, which is passed through fully connected layers to predict object class scores.
Fig. 2: Overview of our pipeline. TRELLIS generates a 3DGS asset from four multi-view images of the vehicle. The asset is converted into a point cloud, which is processed by the kinematic reasoning module for part-level segmentation and motion axes estimation. In parallel, SAGA segments the 3DGS representation directly, with additional movable part masks integrated alongside auto-segmented ones. The resulting clusters are aligned with the PointNet++ segmentation outputs to retain six target parts. Finally, part geometries are refined at the boundary in the part-edge refinement module, and all segmented parts with their predicted motion axes are combined to produce an animatable 3DGS vehicle.
Fig. 3: PointNet++ training data examples. Point cloud data are colored according to their part label, with joint and hinge positions marked as black balls.
Fig. 4: Rendering of all car parts before and after the boundary finetuning step
Fig. 5: Qualitative comparison of part-manipulated vehicle renderings across different ablation pipelines. From top to bottom: (1) SAGA + Trellis, (2) + Mask Insertion, (3) + Boundary Finetuning (ours), and (4) Ground Truth. Mask insertion leads to more complete part clustering, while the final boundary finetuning module produces cleaner edges and reduces floating Gaussians, yielding renderings that align more closely with the ground-truth vehicles in perceptual quality.
Components
CLIP (↑)
LPIPS (↓)
TRELLIS + SAGA
0.848
0.305
TRELLIS + SAGA + Mask insertion
0.855
0.304
TRELLIS + SAGA + Mask insertion + Edge ft (ours)
0.873
0.283
TABLE I: Ablation study of our pipeline. Performance with different components enabled are recorded. Adding mask insertion improves similarity, while the full pipeline with boundary finetuning produces the best result – the highest CLIP similarity (↑) and lowest LPIPS distance (↓) .