Yong Ding

Jiale Li, Hang Dai, Hao Han et al.

LiDAR and camera are two modalities available for 3D semantic segmentation in autonomous driving. The popular LiDAR-only methods severely suffer from inferior segmentation on small and distant objects due to insufficient laser points, while the robust multi-modal solution is under-explored, where we investigate three crucial inherent difficulties: modality heterogeneity, limited sensor field of view intersection, and multi-modal data augmentation. We propose a multi-modal 3D semantic segmentation model (MSeg3D) with joint intra-modal feature extraction and inter-modal feature fusion to mitigate the modality heterogeneity. The multi-modal fusion in MSeg3D consists of geometry-based feature fusion GF-Phase, cross-modal feature completion, and semantic-based feature fusion SF-Phase on all visible points. The multi-modal data augmentation is reinvigorated by applying asymmetric transformations on LiDAR point cloud and multi-camera images individually, which benefits the model training with diversified augmentation transformations. MSeg3D achieves state-of-the-art results on nuScenes, Waymo, and SemanticKITTI datasets. Under the malfunctioning multi-camera input and the multi-frame point clouds input, MSeg3D still shows robustness and improves the LiDAR-only baseline. Our code is publicly available at \url{https://github.com/jialeli1/lidarseg3d}.

Yi-Nan Chen, Hang Dai, Yong Ding

Pseudo-LiDAR 3D detectors have made remarkable progress in monocular 3D detection by enhancing the capability of perceiving depth with depth estimation networks, and using LiDAR-based 3D detection architectures. The advanced stereo 3D detectors can also accurately localize 3D objects. The gap in image-to-image generation for stereo views is much smaller than that in image-to-LiDAR generation. Motivated by this, we propose a Pseudo-Stereo 3D detection framework with three novel virtual view generation methods, including image-level generation, feature-level generation, and feature-clone, for detecting 3D objects from a single image. Our analysis of depth-aware learning shows that the depth loss is effective in only feature-level virtual view generation and the estimated depth map is effective in both image-level and feature-level in our framework. We propose a disparity-wise dynamic convolution with dynamic kernels sampled from the disparity feature map to filter the features adaptively from a single image for generating virtual image features, which eases the feature degradation caused by the depth estimation errors. Till submission (November 18, 2021), our Pseudo-Stereo 3D detection framework ranks 1st on car, pedestrian, and cyclist among the monocular 3D detectors with publications on the KITTI-3D benchmark. The code is released at https://github.com/revisitq/Pseudo-Stereo-3D.

Yu Hong, Qian Liu, Huayuan Cheng et al.

The past few years have witnessed the rapid development of vision-centric 3D perception in autonomous driving. Although the 3D perception models share many structural and conceptual similarities, there still exist gaps in their feature representations, data formats, and objectives, posing challenges for unified and efficient 3D perception framework design. In this paper, we present UniVision, a simple and efficient framework that unifies two major tasks in vision-centric 3D perception, \ie, occupancy prediction and object detection. Specifically, we propose an explicit-implicit view transform module for complementary 2D-3D feature transformation. We propose a local-global feature extraction and fusion module for efficient and adaptive voxel and BEV feature extraction, enhancement, and interaction. Further, we propose a joint occupancy-detection data augmentation strategy and a progressive loss weight adjustment strategy which enables the efficiency and stability of the multi-task framework training. We conduct extensive experiments for different perception tasks on four public benchmarks, including nuScenes LiDAR segmentation, nuScenes detection, OpenOccupancy, and Occ3D. UniVision achieves state-of-the-art results with +1.5 mIoU, +1.8 NDS, +1.5 mIoU, and +1.8 mIoU gains on each benchmark, respectively. We believe that the UniVision framework can serve as a high-performance baseline for the unified vision-centric 3D perception task. The code will be available at \url{https://github.com/Cc-Hy/UniVision}.

Xi Zhao, Yong Ding, Quanyu Zhao

In this paper a new multi-candidate electronic voting scheme is constructed with unlimited participants. The main idea is to express a ballot to allow voting for up to k out of the m candidates and unlimited participants. The purpose of vote is to select more than one winner among $m$ candidates. Our result is complementary to the result by Sun peiyong$'$ s scheme, in the sense, their scheme is not amenable for large-scale electronic voting due to flaw of ballot structure. In our scheme the vote is split and hidden, and tallying is made for $G\ddot{o}del$ encoding in decimal base without any trusted third party, and the result does not rely on any traditional cryptography or computational intractable assumption. Thus the proposed scheme not only solves the problem of ballot structure, but also achieves the security including perfect ballot secrecy, receipt-free, robustness, fairness and dispute-freeness.