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Yuan Ren

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7 papers

2 author rows

ICRA Conference 2025 Conference Paper

AutoSplat: Constrained Gaussian Splatting for Autonomous Driving Scene Reconstruction

Mustafa Khan
Hamidreza Fazlali
Dhruv Sharma
Tongtong Cao
Dongfeng Bai
Yuan Ren
Bingbing Liu

Realistic scene reconstruction and view synthesis are essential for advancing autonomous driving systems by simulating safety-critical scenarios. 3D Gaussian Splatting (3DGS) excels in real-time rendering and static scene reconstructions but struggles with modeling driving scenarios due to complex backgrounds, dynamic objects, and sparse camera views. We propose AutoSplat, a framework employing Gaussian splatting to realistically reconstruct autonomous driving scenes. By imposing geometric constraints on Gaussians representing the road and sky regions, our method enables multi-view consistent simulation of challenging scenarios, including lane changes. Leveraging 3D templates, we introduce a reflected Gaussian consistency constraint to supervise both the visible and unseen side of foreground objects. Moreover, to model the dynamic appearance of foreground objects, we estimate temporally-dependent residual spherical harmonics for each foreground Gaussian. Extensive experiments on Pandaset [1] and KITTI [2] demonstrate that AutoSplat outperforms state-of-the-art methods in scene reconstruction and novel view synthesis across diverse driving scenarios. Our project page can be found here: https://autosplat.github.io/

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ICRA Conference 2024 Conference Paper

Uplifting Range-View-based 3D Semantic Segmentation in Real-Time with Multi-Sensor Fusion

Shiqi Tan
Hamidreza Fazlali
Yixuan Xu
Yuan Ren
Bingbing Liu

Range-View(RV)-based 3D point cloud segmentation is widely adopted due to its compact data form. However, RV-based methods fall short in providing robust segmentation for the occluded points and suffer from distortion of projected RGB images due to the sparse nature of 3D point clouds. To alleviate these problems, we propose a new LiDAR and Camera Range-view-based 3D point cloud semantic segmentation method (LaCRange). Specifically, a distortion-compensating knowledge distillation (DCKD) strategy is designed to remedy the adverse effect of RV projection of RGB images. Moreover, a context-based feature fusion module is introduced for robust and preservative sensor fusion. Finally, in order to address the limited resolution of RV and its insufficiency of 3D topology, a new point refinement scheme is devised for proper aggregation of features in 2D and augmentation of point features in 3D. We evaluated the proposed method on large-scale autonomous driving datasets i. e. SemanticKITTI and nuScenes. In addition to being real-time, the proposed method achieves state-of-the-art results on nuScenes benchmark.

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ICRA Conference 2023 Conference Paper

PCGen: Point Cloud Generator for LiDAR Simulation

Chenqi Li
Yuan Ren
Bingbing Liu

Data is a fundamental building block for LiDAR perception systems. Unfortunately, real-world data collection and annotation is extremely costly & laborious. Recently, real data based LiDAR simulators have shown tremendous potential to complement real data, due to their scalability and high-fidelity compared to graphics engine based methods. Before simulation can be deployed in the real-world, two shortcomings need to be addressed. First, existing methods usually generate data which are more noisy and complete than the real point clouds, due to 3D reconstruction error and pure geometry-based raycasting method. Second, prior works on simulation for object detection focus solely on rigid objects, like cars, but Vulnerable Road User (VRU)s, like pedestrians, are important road participants. To tackle the first challenge, we propose First Peak Averaging (FPA) raycasting and surrogate model raydrop. FPA enables the simulation of both point cloud coordinates and sensor features, while taking into account reconstruction noise. The ray-wise surrogate raydrop model mimics the physical properties of LiDAR's laser receiver to determine whether a simulated point would be recorded by a real LiDAR. With minimal training data, the surrogate model can generalize to different geographies and scenes, closing the domain gap between raycasted and real point clouds. To tackle the simulation of deformable VRU simulation, we employ Skinned Multi-Person Linear model (SMPL) dataset to provide a pedestrian simulation baseline and compare the domain gap between CAD and reconstructed objects. Applying our pipeline to perform novel sensor synthesis, results show that object detection models trained by simulation data can achieve similar result as the real data trained model.

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ICRA Conference 2022 Conference Paper

Object Insertion Based Data Augmentation for Semantic Segmentation

Yuan Ren
Siyan Zhao
Bingbing Liu

Neural network used for the LiDAR semantic segmentation task needs the point-wise labeled point clouds for training, which is more expensive than bounding box annotations. Enhancing the diversity of training data through object insertion is an effective method to reduce labeling costs. The existing object insertion methods are mainly divided into two categories. First is “copy” the clusters from a LiDAR frame and “paste” it to other frames or positions. Second is inserting CAD models into the background then using LiDAR simulator to generate laser points of the inserted CAD models. “Copy-paste” method cannot generate realistic scanning lines and shadows, and the CAD models, especially the CAD models of flexible objects, are hard to obtain. We propose an object insertion based data augmentation method which can increase the performance of the semantic segmentation network remarkably. First, an object library is created by using the labeled LiDAR point clouds. Then, these objects are inserted into the LiDAR point clouds dynamically during the training. Finally, the realistic scanning lines and shadows are simulated according to the real LiDAR parameters. The experimental results show that the proposed augmentation method can increase the performance of different semantic segmentation frameworks remarkably.

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ICRA Conference 2021 Conference Paper

S3Net: 3D LiDAR Sparse Semantic Segmentation Network

Ran Cheng
Ryan Razani
Yuan Ren
Bingbing Liu

Semantic Segmentation is a crucial component in the perception systems of many applications, such as robotics and autonomous driving that rely on accurate environmental perception and understanding. In literature, several approaches are introduced to attempt LiDAR semantic segmentation task, such as projection-based (range-view or birds-eye-view), and voxel-based approaches. However, they either abandon the valuable 3D topology and geometric relations and suffer from information loss introduced in the projection process or are inefficient. Therefore, there is a need for accurate models capable of processing the 3D driving-scene point cloud in 3D space. In this paper, we propose S3Net, a novel convolutional neural network for LiDAR point cloud semantic segmentation. It adopts an encoder-decoder backbone that consists of Sparse Intra-channel Attention Module (SIntraAM), and Sparse Inter-channel Attention Module (SInterAM) to emphasize the fine details of both within each feature map and among nearby feature maps. To extract the global contexts in deeper layers, we introduce Sparse Residual Tower based upon sparse convolution that suits varying sparsity of LiDAR point cloud. In addition, geo-aware anisotrophic loss is leveraged to emphasize the semantic boundaries and penalize the noise within each predicted regions, leading to a robust prediction. Our experimental results show that the proposed method leads to a large improvement (12%) compared to its baseline counterpart (MinkNet42 [1]) on SemanticKITTI [2] test set and achieves state-of-the-art mIoU accuracy of semantic segmentation approaches.

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AIJ Journal 2016 Journal Article

Tractable approximate deduction for OWL

Jeff Z. Pan
Yuan Ren
Yuting Zhao

Today's ontology applications require efficient and reliable description logic (DL) reasoning services. Expressive DLs usually have high worst case complexity while tractable DLs are restricted in terms of expressive power. This brings a new challenge: can users use expressive DLs to build their ontologies and still enjoy the efficient services as in tractable languages? Approximation has been considered as a solution to this challenge; however, traditional approximation approaches have limitations in terms of performance and usability. In this paper, we present a tractable approximate reasoning framework for OWL 2 that improves efficiency and guarantees soundness. Evaluation on ontologies from benchmarks and real-world use cases shows that our approach can do reasoning on complex ontologies efficiently with a high recall.

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AAAI Conference 2010 Conference Paper

Soundness Preserving Approximation for TBox Reasoning

Yuan Ren
Jeff Pan
Yuting Zhao

Large scale ontology applications require efficient and robust description logic (DL) reasoning services. Expressive DLs usually have very high worst case complexity while tractable DLs are restricted in terms of expressive power. This brings a new challenge: can users use expressive DLs to build their ontologies and still enjoy the efficient services as in tractable languages. In this paper, we present a soundness preserving approximate reasoning framework for TBox reasoning in OWL2-DL. The ontologies are encoded into EL++ with additional data structures. A tractable algorithm is presented to classify such approximation by realizing more and more inference patterns. Preliminary evaluation shows that our approach can classify existing benchmarks in large scale efficiently with a high recall.

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