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Li Auto Inc.

Industry researchasia · cn
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Research library202linked papers
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Selected work

Representative Papers

MVGS: Multi-view-regulated Gaussian Splatting for Novel View Synthesis

Oct 02, 2024arXiv.org

To address overfitting in 3D Gaussian Splatting (3DGS) under single-view supervision—leading to artifacts in novel-view synthesis and inaccurate geometric reconstruction—this paper proposes a multi-view collaborative optimization framework. Our method introduces three key innovations: (1) a novel multi-view regularization paradigm that jointly enforces consistency across multiple views; (2) an intrinsic-cross-guided coarse-to-fine training strategy integrating multi-scale geometric and appearance priors; and (3) ray-intersection-driven cross-view densification coupled with view-difference-aware adaptive densification. While preserving real-time rendering performance, our approach significantly improves both novel-view image fidelity and 3D geometric accuracy. Extensive experiments demonstrate strong generalization across diverse scenes and mainstream 3DGS variants, outperforming existing single-view methods in both qualitative and quantitative evaluations.

15 citations1 influentialRead paper

RubricHub: A Comprehensive and Highly Discriminative Rubric Dataset via Automated Coarse-to-Fine Generation

Jan 13, 2026

This work addresses the challenge that open-ended generation tasks lack verifiable, fine-grained scoring rubrics, which limits the effectiveness of rule-based reinforcement learning. To overcome this, the authors propose an automated coarse-to-fine rubric generation framework that leverages principle-guided synthesis, multi-model aggregation, and a difficulty evolution mechanism to construct high-quality, highly discriminative scoring criteria. This framework enables the first large-scale, multi-domain, fine-grained, and scalable automatic evaluation system. Integrating the generated rubrics with Rejection Sampling Fine-Tuning (RuFT) and Rubric-guided Reinforcement Learning (RuRL), a Qwen3-14B model trained on RubricHub achieves a score of 69.3 on HealthBench, surpassing closed-source models such as GPT-5 and establishing a new state-of-the-art performance.

1 citationsRead paper

InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit Fields

Jan 06, 2026arXiv.org

This work addresses the limitations of conventional depth estimation methods, which are constrained by discrete image grids and thus struggle to recover fine geometric details or support arbitrary-resolution outputs. To overcome this, we propose a continuous depth representation based on neural implicit fields, introducing a local implicit decoder that enables high-fidelity depth querying at any 2D coordinate. To facilitate training and evaluation, we construct a high-resolution 4K synthetic dataset. Experimental results demonstrate that our approach achieves state-of-the-art performance on both synthetic and real-world datasets, significantly enhancing geometric detail recovery and substantially improving the quality of novel-view synthesis under large viewpoint changes.

1 citationsRead paper
Recent publications

Latest Papers

PCT-Prompt: A Prompt-Guided Transformer Framework for Dense Prediction Tasks in Point Clouds

Aug 17, 2026

This study addresses the performance limitations of standard Transformers in point cloud dense prediction caused by weak prior assumptions. To overcome this, we propose PCT-Prompt, a novel framework incorporating a prompt-guided feature branch and a prompt dropout mechanism. By integrating a geometry-sensitive abstraction layer with cross-attention optimization, the method effectively fuses multi-scale geometric features with global regularization, achieving a dynamic balance between local details and global consistency. Extensive experiments on ShapeNetPart, S3DIS, and DALES datasets demonstrate that PCT-Prompt significantly improves dense prediction performance. These results validate the framework's effectiveness and robustness in handling complex 3D scenes, offering a promising solution for enhancing Transformer-based architectures in point cloud analysis tasks where strong inductive biases are typically lacking.

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