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Chenxu Luo

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3 papers

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IROS Conference 2024 Conference Paper

Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point Clouds

Mu Cai
Chenxu Luo
Yong Jae Lee
Xiaodong Yang 0001

3D perception in LiDAR point clouds is crucial for a self-driving vehicle to properly act in 3D environment. However, manually labeling point clouds is hard and costly. There has been a growing interest in self-supervised pre-training of 3D perception models. Following the success of contrastive learning in images, current methods mostly conduct contrastive pre-training on point clouds only. Yet an autonomous driving vehicle is typically supplied with multiple sensors including cameras and LiDAR. In this context, we systematically study single modality, cross-modality, and multi-modality for contrastive learning of point clouds, and show that cross-modality wins over other alternatives. In addition, considering the huge difference between the training sources in 2D images and 3D point clouds, it remains unclear how to design more effective contrastive units for LiDAR. We therefore propose the instance-aware and similarity-balanced contrastive units that are tailored for self-driving point clouds. Extensive experiments reveal that our approach achieves remarkable performance gains over various point cloud models across the downstream perception tasks of LiDAR based 3D object detection and 3D semantic segmentation on the four popular benchmarks including Waymo Open Dataset, nuScenes, SemanticKITTI and ONCE.

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IROS Conference 2020 Conference Paper

Probabilistic Multi-modal Trajectory Prediction with Lane Attention for Autonomous Vehicles

Chenxu Luo
Lin Sun 0004
Dariush Dabiri
Alan L. Yuille

Trajectory prediction is crucial for autonomous vehicles. The planning system not only needs to know the current state of the surrounding objects but also their possible states in the future. As for vehicles, their trajectories are significantly influenced by the lane geometry and how to effectively use the lane information is of active interest. Most of the existing works use rasterized maps to explore road information, which does not distinguish different lanes. In this paper, we propose a novel instance-aware representation for lane representation. By integrating the lane features and trajectory features, a goal-oriented lane attention module is proposed to predict the future locations of the vehicle. We show that the proposed lane representation together with the lane attention module can be integrated into the widely used encoder-decoder framework to generate diverse predictions. Most importantly, each generated trajectory is associated with a probability to handle the uncertainty. Our method does not suffer from collapsing to one behavior modal and can cover diverse possibilities. Extensive experiments and ablation studies on the benchmark datasets corroborate the effectiveness of our proposed method. Notably, our proposed method ranks third place in the Argoverse motion forecasting competition at NeurIPS 2019 1.

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AAAI Conference 2017 Conference Paper

Learning Discriminative Activated Simplices for Action Recognition

Chenxu Luo
Chang Ma
Chunyu Wang
Yizhou Wang

We address the task of action recognition from a sequence of 3D human poses. This is a challenging task firstly because the poses of the same class could have large intra-class variations either caused by inaccurate 3D pose estimation or various performing styles. Also different actions, e.g., walking vs. jogging, may share similar poses which makes the representation not discriminative to differentiate the actions. To solve the problems, we propose a novel representation for 3D poses by a mixture of Discriminative Activated Simplices (DAS). Each DAS consists of a few bases and represent pose data by their convex combinations. The discriminative power of DAS is firstly realized by learning discriminative bases across classes with a block diagonal constraint enforced on the basis coefficient matrix. Secondly, the DAS provides tight characterization of the pose manifolds thus reducing the chance of generating overlapped DAS between similar classes. We justify the power of the model on benchmark datasets and witness consistent performance improvements.

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