no code implementations • 3 Apr 2024 • Bingnan Ni, Huanyu Wang, Dongfeng Bai, Minghe Weng, Dexin Qi, Weichao Qiu, Bingbing Liu
In this paper, we design a novel inference pattern that encourages a single camera ray possessing more contextual information, and models the relationship among sample points on each camera ray.
no code implementations • 19 Mar 2024 • HongYu Zhou, Jiahao Shao, Lu Xu, Dongfeng Bai, Weichao Qiu, Bingbing Liu, Yue Wang, Andreas Geiger, Yiyi Liao
Holistic understanding of urban scenes based on RGB images is a challenging yet important problem.
1 code implementation • 16 Jan 2024 • Xu Yan, Haiming Zhang, Yingjie Cai, Jingming Guo, Weichao Qiu, Bin Gao, Kaiqiang Zhou, Yue Zhao, Huan Jin, Jiantao Gao, Zhen Li, Lihui Jiang, Wei zhang, Hongbo Zhang, Dengxin Dai, Bingbing Liu
The rise of large foundation models, trained on extensive datasets, is revolutionizing the field of AI.
no code implementations • 19 Dec 2023 • 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.
no code implementations • 28 Sep 2023 • Zheyuan Yang, Yibo Liu, Guile Wu, Tongtong Cao, Yuan Ren, Yang Liu, Bingbing Liu
To resolve this problem, we study learning effective NeRFs and SDFs representations with 3D Generative Adversarial Networks (GANs) for 3D object generation.
no code implementations • ICCV 2023 • Yibo Liu, Kelly Zhu, Guile Wu, Yuan Ren, Bingbing Liu, Yang Liu, Jinjun Shan
This set-level latent code is an expression of the optimal 3D shape in the implicit space, and can be subsequently decoded to a continuous SDF of the vehicle.
no code implementations • 9 Jun 2023 • Eduardo R. Corral-Soto, Alaap Grandhi, Yannis Y. He, Mrigank Rochan, Bingbing Liu
In recent years, much progress has been made in LiDAR-based 3D object detection mainly due to advances in detector architecture designs and availability of large-scale LiDAR datasets.
no code implementations • CVPR 2023 • Chen Yang, Peihao Li, Zanwei Zhou, Shanxin Yuan, Bingbing Liu, Xiaokang Yang, Weichao Qiu, Wei Shen
We present NeRFVS, a novel neural radiance fields (NeRF) based method to enable free navigation in a room.
no code implementations • 2 Feb 2023 • YiXuan Xu, Hamidreza Fazlali, Yuan Ren, Bingbing Liu
In this method, a dual-task 3D backbone is developed to extract both panoptic- and detection-level features from the input LiDAR point cloud.
no code implementations • ICCV 2023 • Peihao Li, Shaohui Wang, Chen Yang, Bingbing Liu, Weichao Qiu, Haoqian Wang
Neural radiance fields (NeRF) achieve impressive performance in novel view synthesis when trained on only single sequence data.
no code implementations • ICCV 2023 • Guile Wu, Tongtong Cao, Bingbing Liu, Xingxin Chen, Yuan Ren
In this work, we propose the first attempt to explore multi-domain learning and generalization for LiDAR-based 3D object detection.
no code implementations • 18 Oct 2022 • Mrigank Rochan, Xingxin Chen, Alaap Grandhi, Eduardo R. Corral-Soto, Bingbing Liu
The idea is to initiate the training with the batch of samples from the source and target domain data in an alternate fashion, but then gradually reduce the amount of the source domain data over time as the training progresses.
no code implementations • 17 Oct 2022 • Chenqi Li, Yuan Ren, Bingbing Liu
To tackle the first challenge, we propose FPA raycasting and surrogate model raydrop.
no code implementations • CVPR 2022 • Hamidreza Fazlali, YiXuan Xu, Yuan Ren, Bingbing Liu
In our method, the 3D object detection backbone in Bird's-Eye-View (BEV) plane is augmented by the injection of Range-View (RV) feature maps from the 3D panoptic segmentation backbone.
no code implementations • 2 Nov 2021 • Enxu Li, Ryan Razani, YiXuan Xu, Bingbing Liu
A fast and accurate panoptic segmentation system for LiDAR point clouds is crucial for autonomous driving vehicles to understand the surrounding objects and scenes.
no code implementations • 8 Oct 2021 • Dongfeng Bai, Tongtong Cao, Jingming Guo, Bingbing Liu
Curbs are one of the essential elements of urban and highway traffic environments.
no code implementations • 20 Jul 2021 • Mrigank Rochan, Shubhra Aich, Eduardo R. Corral-Soto, Amir Nabatchian, Bingbing Liu
In this paper, we focus on a less explored, but more realistic and complex problem of domain adaptation in LiDAR semantic segmentation.
no code implementations • 8 Feb 2021 • Ran Cheng, Ryan Razani, Ehsan Taghavi, Enxu Li, Bingbing Liu
Autonomous robotic systems and self driving cars rely on accurate perception of their surroundings as the safety of the passengers and pedestrians is the top priority.
Ranked #3 on 3D Semantic Segmentation on nuScenes
1 code implementation • 1 Sep 2020 • Shubhra Aich, Jean Marie Uwabeza Vianney, Md Amirul Islam, Mannat Kaur, Bingbing Liu
In this paper, we propose a Bidirectional Attention Network (BANet), an end-to-end framework for monocular depth estimation (MDE) that addresses the limitation of effectively integrating local and global information in convolutional neural networks.
no code implementations • 24 Aug 2020 • Martin Gerdzhev, Ryan Razani, Ehsan Taghavi, Bingbing Liu
Semantic segmentation of point clouds is a key component of scene understanding for robotics and autonomous driving.
Ranked #15 on 3D Semantic Segmentation on SemanticKITTI
no code implementations • 21 Nov 2019 • Jean Marie Uwabeza Vianney, Shubhra Aich, Bingbing Liu
In this paper, we strive for solving the ambiguities arisen by the astoundingly high density of raw PseudoLiDAR for monocular 3D object detection for autonomous driving.
no code implementations • 19 Aug 2019 • Ehsan Nezhadarya, Yang Liu, Bingbing Liu
We present a learning-based method to estimate the object bounding box from its 2D bird's-eye view (BEV) LiDAR points.
no code implementations • CVPR 2020 • Ehsan Nezhadarya, Ehsan Taghavi, Ryan Razani, Bingbing Liu, Jun Luo
While several convolution-like operators have recently been proposed for extracting features out of point clouds, down-sampling an unordered point cloud in a deep neural network has not been rigorously studied.