1 code implementation • 1 Apr 2024 • Jianqiao Zheng, Xueqian Li, Simon Lucey
By contrast, convolutional neural networks (CNNs) have an architectural inductive bias enabling them to perform well on such problems.
no code implementations • 24 Mar 2024 • Dongrui Liu, Daqi Liu, Xueqian Li, Sihao Lin, Hongwei Xie, Bing Wang, Xiaojun Chang, Lei Chu
Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown remarkable adaptability in the context of large out-of-distribution autonomous driving.
1 code implementation • 9 Mar 2024 • Xueqian Li, Simon Lucey
In contrast to current state-of-the-art methods, such as NSFP [25], which employ deep implicit neural functions for modeling scene flow, we present a novel approach that utilizes classical kernel representations.
1 code implementation • 23 Jan 2024 • Jianqiao Zheng, Xueqian Li, Simon Lucey
Training vision transformer networks on small datasets poses challenges.
1 code implementation • 16 Oct 2023 • Kavisha Vidanapathirana, Shin-Fang Chng, Xueqian Li, Simon Lucey
The test-time optimization of scene flow - using a coordinate network as a neural prior - has gained popularity due to its simplicity, lack of dataset bias, and state-of-the-art performance.
1 code implementation • 1 Sep 2023 • Jianqiao Zheng, Xueqian Li, Sameera Ramasinghe, Simon Lucey
End-to-end trained per-point embeddings are an essential ingredient of any state-of-the-art 3D point cloud processing such as detection or alignment.
1 code implementation • ICCV 2023 • Xueqian Li, Jianqiao Zheng, Francesco Ferroni, Jhony Kaesemodel Pontes, Simon Lucey
Neural Scene Flow Prior (NSFP) is of significant interest to the vision community due to its inherent robustness to out-of-distribution (OOD) effects and its ability to deal with dense lidar points.
Ranked #4 on Self-supervised Scene Flow Estimation on Argoverse 2
1 code implementation • 18 May 2022 • Jianqiao Zheng, Sameera Ramasinghe, Xueqian Li, Simon Lucey
It is well noted that coordinate-based MLPs benefit -- in terms of preserving high-frequency information -- through the encoding of coordinate positions as an array of Fourier features.
no code implementations • CVPR 2022 • Chaoyang Wang, Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey
Here, we propose a neural trajectory prior to capture continuous spatio-temporal information without the need for offline data.
1 code implementation • NeurIPS 2021 • Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey
A central innovation here is the inclusion of a neural scene flow prior, which uses the architecture of neural networks as a new type of implicit regularizer.
Ranked #2 on Self-supervised Scene Flow Estimation on Argoverse 2
1 code implementation • CVPR 2021 • Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey
We address the generalization ability of recent learning-based point cloud registration methods.
1 code implementation • 12 Dec 2019 • Vinit Sarode, Xueqian Li, Hunter Goforth, Yasuhiro Aoki, Animesh Dhagat, Rangaprasad Arun Srivatsan, Simon Lucey, Howie Choset
We perform extensive simulation and real-world experiments to validate the efficacy of our approach and compare the performance with state-of-art approaches.
6 code implementations • 21 Aug 2019 • Vinit Sarode, Xueqian Li, Hunter Goforth, Yasuhiro Aoki, Rangaprasad Arun Srivatsan, Simon Lucey, Howie Choset
PointNet has recently emerged as a popular representation for unstructured point cloud data, allowing application of deep learning to tasks such as object detection, segmentation and shape completion.
no code implementations • 26 Feb 2019 • Peng Yin, Rangaprasad Arun Srivatsan, Yin Chen, Xueqian Li, Hongda Zhang, Lingyun Xu, Lu Li, Zhenzhong Jia, Jianmin Ji, Yuqing He
We propose MRS-VPR, a multi-resolution, sampling-based place recognition method, which can significantly improve the matching efficiency and accuracy in sequential matching.
no code implementations • 26 Feb 2019 • Peng Yin, Lingyun Xu, Xueqian Li, Chen Yin, Yingli Li, Rangaprasad Arun Srivatsan, Lu Li, Jianmin Ji, Yuqing He
Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically.