Yuenan Hou

AAAI Conference 2026 Conference Paper

RacketVision: A Multiple Racket Sports Benchmark for Unified Ball and Racket Analysis

• Linfeng Dong
• Yuchen Yang
• Hao Wu
• Wei Wang
• Yuenan Hou
• Zhihang Zhong
• Xiao Sun

We introduce RacketVision, a novel dataset and benchmark for advancing computer vision in sports analytics, covering table tennis, tennis, and badminton. The dataset is the first to provide large-scale, fine-grained annotations for racket pose alongside traditional ball positions, enabling research into complex human-object interactions. It is designed to tackle three interconnected tasks: fine-grained ball tracking, articulated racket pose estimation, and predictive ball trajectory forecasting. Our evaluation of established baselines reveals a critical insight for multi-modal fusion: while naively concatenating racket pose features degrades performance, a Cross-Attention mechanism is essential to unlock their value, leading to trajectory prediction results that surpass strong unimodal baselines. RacketVision provides a versatile resource and a strong starting point for future research in dynamic object tracking, conditional motion forecasting, and multi-modal analysis in sports.

IROS Conference 2025 Conference Paper

Towards Label-Free 3D Visual Grounding with Vision Foundation Models

• Xiaopei Wu
• Yuenan Hou
• Binbin Lin
• Xinge Zhu
• Yuexin Ma
• Haifeng Liu 0001
• Deng Cai 0001
• Xiao Sun

AAAI Conference 2024 Conference Paper

Frozen CLIP Transformer Is an Efficient Point Cloud Encoder

• Xiaoshui Huang
• Zhou Huang
• Sheng Li
• Wentao Qu
• Tong He
• Yuenan Hou
• Yifan Zuo
• Wanli Ouyang

The pretrain-finetune paradigm has achieved great success in NLP and 2D image fields because of the high-quality representation ability and transferability of their pretrained models. However, pretraining such a strong model is difficult in the 3D point cloud field due to the limited amount of point cloud sequences. This paper introduces Efficient Point Cloud Learning (EPCL), an effective and efficient point cloud learner for directly training high-quality point cloud models with a frozen CLIP transformer. Our EPCL connects the 2D and 3D modalities by semantically aligning the image features and point cloud features without paired 2D-3D data. Specifically, the input point cloud is divided into a series of local patches, which are converted to token embeddings by the designed point cloud tokenizer. These token embeddings are concatenated with a task token and fed into the frozen CLIP transformer to learn point cloud representation. The intuition is that the proposed point cloud tokenizer projects the input point cloud into a unified token space that is similar to the 2D images. Comprehensive experiments on 3D detection, semantic segmentation, classification and few-shot learning demonstrate that the CLIP transformer can serve as an efficient point cloud encoder and our method achieves promising performance on both indoor and outdoor benchmarks. In particular, performance gains brought by our EPCL are 19.7 AP50 on ScanNet V2 detection, 4.4 mIoU on S3DIS segmentation and 1.2 mIoU on SemanticKITTI segmentation compared to contemporary pretrained models. Code is available at \url{https://github.com/XiaoshuiHuang/EPCL}.

AAAI Conference 2024 Conference Paper

Semi-supervised 3D Object Detection with PatchTeacher and PillarMix

• Xiaopei Wu
• Liang Peng
• Liang Xie
• Yuenan Hou
• Binbin Lin
• Xiaoshui Huang
• Haifeng Liu
• Deng Cai

Semi-supervised learning aims to leverage numerous unlabeled data to improve the model performance. Current semi-supervised 3D object detection methods typically use a teacher to generate pseudo labels for a student, and the quality of the pseudo labels is essential for the final performance. In this paper, we propose PatchTeacher, which focuses on partial scene 3D object detection to provide high-quality pseudo labels for the student. Specifically, we divide a complete scene into a series of patches and feed them to our PatchTeacher sequentially. PatchTeacher leverages the low memory consumption advantage of partial scene detection to process point clouds with a high-resolution voxelization, which can minimize the information loss of quantization and extract more fine-grained features. However, it is non-trivial to train a detector on fractions of the scene. Therefore, we introduce three key techniques, i.e., Patch Normalizer, Quadrant Align, and Fovea Selection, to improve the performance of PatchTeacher. Moreover, we devise PillarMix, a strong data augmentation strategy that mixes truncated pillars from different LiDAR scans to generate diverse training samples and thus help the model learn more general representation. Extensive experiments conducted on Waymo and ONCE datasets verify the effectiveness and superiority of our method and we achieve new state-of-the-art results, surpassing existing methods by a large margin. Codes are available at https://github.com/LittlePey/PTPM.

NeurIPS Conference 2023 Conference Paper

CluB: Cluster Meets BEV for LiDAR-Based 3D Object Detection

• Yingjie Wang
• Jiajun Deng
• Yuenan Hou
• Yao Li
• Yu Zhang
• Jianmin Ji
• Wanli Ouyang
• Yanyong Zhang

Currently, LiDAR-based 3D detectors are broadly categorized into two groups, namely, BEV-based detectors and cluster-based detectors. BEV-based detectors capture the contextual information from the Bird's Eye View (BEV) and fill their center voxels via feature diffusion with a stack of convolution layers, which, however, weakens the capability of presenting an object with the center point. On the other hand, cluster-based detectors exploit the voting mechanism and aggregate the foreground points into object-centric clusters for further prediction. In this paper, we explore how to effectively combine these two complementary representations into a unified framework. Specifically, we propose a new 3D object detection framework, referred to as CluB, which incorporates an auxiliary cluster-based branch into the BEV-based detector by enriching the object representation at both feature and query levels. Technically, CluB is comprised of two steps. First, we construct a cluster feature diffusion module to establish the association between cluster features and BEV features in a subtle and adaptive fashion. Based on that, an imitation loss is introduced to distill object-centric knowledge from the cluster features to the BEV features. Second, we design a cluster query generation module to leverage the voting centers directly from the cluster branch, thus enriching the diversity of object queries. Meanwhile, a direction loss is employed to encourage a more accurate voting center for each cluster. Extensive experiments are conducted on Waymo and nuScenes datasets, and our CluB achieves state-of-the-art performance on both benchmarks.

NeurIPS Conference 2023 Conference Paper

RangePerception: Taming LiDAR Range View for Efficient and Accurate 3D Object Detection

• Yeqi BAI
• Ben Fei
• Youquan Liu
• Tao Ma
• Yuenan Hou
• Botian Shi
• Yikang Li

LiDAR-based 3D detection methods currently use bird's-eye view (BEV) or range view (RV) as their primary basis. The former relies on voxelization and 3D convolutions, resulting in inefficient training and inference processes. Conversely, RV-based methods demonstrate higher efficiency due to their compactness and compatibility with 2D convolutions, but their performance still trails behind that of BEV-based methods. To eliminate this performance gap while preserving the efficiency of RV-based methods, this study presents an efficient and accurate RV-based 3D object detection framework termed RangePerception. Through meticulous analysis, this study identifies two critical challenges impeding the performance of existing RV-based methods: 1) there exists a natural domain gap between the 3D world coordinate used in output and 2D range image coordinate used in input, generating difficulty in information extraction from range images; 2) native range images suffer from vision corruption issue, affecting the detection accuracy of the objects located on the margins of the range images. To address the key challenges above, we propose two novel algorithms named Range Aware Kernel (RAK) and Vision Restoration Module (VRM), which facilitate information flow from range image representation and world-coordinate 3D detection results. With the help of RAK and VRM, our RangePerception achieves 3. 25/4. 18 higher averaged L1/L2 AP compared to previous state-of-the-art RV-based method RangeDet, on Waymo Open Dataset. For the first time as an RV-based 3D detection method, RangePerception achieves slightly superior averaged AP compared with the well-known BEV-based method CenterPoint and the inference speed of RangePerception is 1. 3 times as fast as CenterPoint.

AAAI Conference 2019 Conference Paper

Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks

• Yuenan Hou
• Zheng Ma
• Chunxiao Liu
• Chen Change Loy

The training of many existing end-to-end steering angle prediction models heavily relies on steering angles as the supervisory signal. Without learning from much richer contexts, these methods are susceptible to the presence of sharp road curves, challenging traffic conditions, strong shadows, and severe lighting changes. In this paper, we considerably improve the accuracy and robustness of predictions through heterogeneous auxiliary networks feature mimicking, a new and effective training method that provides us with much richer contextual signals apart from steering direction. Specifically, we train our steering angle predictive model by distilling multi-layer knowledge from multiple heterogeneous auxiliary networks that perform related but different tasks, e.g., image segmentation or optical flow estimation. As opposed to multi-task learning, our method does not require expensive annotations of related tasks on the target set. This is made possible by applying contemporary off-the-shelf networks on the target set and mimicking their features in different layers after transformation. The auxiliary networks are discarded after training without affecting the runtime efficiency of our model. Our approach achieves a new state-of-the-art on Udacity and Comma.ai, outperforming the previous best by a large margin of 12.8% and 52.1%1, respectively. Encouraging results are also shown on Berkeley Deep Drive (BDD) dataset.