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Dongfeng Bai

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3 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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AAAI Conference 2024 Conference Paper

RadOcc: Learning Cross-Modality Occupancy Knowledge through Rendering Assisted Distillation

Haiming Zhang
Xu Yan
Dongfeng Bai
Jiantao Gao
Pan Wang
Bingbing Liu
Shuguang Cui
Zhen Li

3D occupancy prediction is an emerging task that aims to estimate the occupancy states and semantics of 3D scenes using multi-view images. However, image-based scene perception encounters significant challenges in achieving accurate prediction due to the absence of geometric priors. In this paper, we address this issue by exploring cross-modal knowledge distillation in this task, i.e., we leverage a stronger multi-modal model to guide the visual model during training. In practice, we observe that directly applying features or logits alignment, proposed and widely used in bird's-eye-view (BEV) perception, does not yield satisfactory results. To overcome this problem, we introduce RadOcc, a Rendering assisted distillation paradigm for 3D Occupancy prediction. By employing differentiable volume rendering, we generate depth and semantic maps in perspective views and propose two novel consistency criteria between the rendered outputs of teacher and student models. Specifically, the depth consistency loss aligns the termination distributions of the rendered rays, while the semantic consistency loss mimics the intra-segment similarity guided by vision foundation models (VLMs). Experimental results on the nuScenes dataset demonstrate the effectiveness of our proposed method in improving various 3D occupancy prediction approaches, e.g., our proposed methodology enhances our baseline by 2.2% in the metric of mIoU and achieves 50% in Occ3D benchmark.

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ICRA Conference 2022 Conference Paper

How to Build a Curb Dataset with LiDAR Data for Autonomous Driving

Dongfeng Bai
Tongtong Cao
Jingming Guo
Bingbing Liu

Curbs are one of the essential elements of urban and highway traffic environments. Robust curb detection provides road structure information for motion planning in an autonomous driving system. Commonly, video cameras and 3D LiDARs are mounted on autonomous vehicles for curb detection. However, camera-based methods suffer from challenging illumination conditions. During the long period of time before wide application of Deep Neural Network (DNN) with point clouds, LiDAR-based curb detection methods are based on hand-crafted features, which suffer from poor detection in some complex scenes. Recently, DNN-based dynamic object detection using LiDAR data has become prevalent, while few works pay attention to curb detection with a DNN approach due to lack of labeled data. A dataset with curb annotations or an efficient curb labeling approach, hence, is of high demand. In this paper, we present how to build a curb dataset with LiDAR data for autonomous driving highly automatically. Firstly, a Semantic High Definition map (SHD map) in a global coordinate frame is generated by applying both SLAM and semantic segmentation on consecutive LiDAR frames. Next, a Road HD map (RHD map) is generated from the SHD map by removing its dynamic noise caused by road users e. g. cars. After that, a Curb Instance map (CI map) can be obtained from the filtered RHD map by a series of curb point extraction and growing. Finally, the CI map can be projected back to single frames for direct, highly automatic curb labeling. In order to validate our proposed labeling method, on top of an open public LiDAR semantic dataset SemanticKITTI [1], an additional curb dataset is built. We run both semantic segmentation and instance segmentation methods on this built dataset. Experimental results show that the curb annotations have good consistency and accuracy. We released this dataset and it is publicly available at https://download.mindspore.cn.

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