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Works72 from public data
- TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers1,106
The proposed TransFusion, a robust solution to LiDAR-camera fusion with a soft-association mechanism to handle inferior image conditions, achieves state-of-the-art performance on large-scale datasets and is extended to the 3D tracking task.
- FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection854
The solution achieves 1st place out of all the vision-only methods in the nuScenes 3D detection challenge of NeurIPS 2020 and proposes a general framework FCOS3D, getting rid of any 2D detection or 2D-3D correspondence priors.
- Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation756
A new framework for the outdoor LiDAR segmentation is proposed, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern while maintaining these inherent properties.
- TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents470
The proposed long short-term memory-based (LSTM-based) realtime traffic prediction algorithm, TrafficPredict, uses an instance layer to learn instances' movements and interactions and has a category layer to learning the similarities of instances belonging to the same type to refine the prediction.
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- CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP273
A Semantic-driven Cross-modal Contrastive Learning framework that pre-trains a 3D network via semantic and spatial-temporal consistency regularization, which significantly outperforms other self-supervised methods and demonstrates the generalizability for handling cross-domain datasets.
- Depth Completion From Sparse LiDAR Data With Depth-Normal Constraints259
A unified CNN framework is proposed that models the geometric constraints between depth and surface normal in a diffusion module and predicts the confidence of sparse LiDAR measurements to mitigate the impact of noise.
- Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation256
This article addresses the problem of distilling knowledge from a large teacher model to a slim student network for LiDAR semantic segmentation by proposing the Point-to-Voxel Knowledge Distillation (PVD), which transfers the hidden knowledge from both point level and voxel level.
- Vision-Centric BEV Perception: A Survey245
This paper provides in-depth analyses and comparative results on various BEV perception tasks, facilitating the evaluation of future works and sparking new research directions, and discusses and shares valuable empirical implementation details to aid in the advancement of related algorithms.
- Rethinking Range View Representation for LiDAR Segmentation220
A range view method is able to surpass the point, voxel, and multi-view fusion counterparts in the competing LiDAR semantic and panoptic segmentation benchmarks, i.e., SemanticKITTI, nuScenes, and ScribbleKITTI.
- Pose Guided Human Video Generation163
A pose guided method to synthesize human videos in a disentangled way: plausible motion prediction and coherent appearance generation and enforce semantic consistency between the generated and ground-truth poses at a high feature level.
- LIF-Seg: LiDAR and Camera Image Fusion for 3D LiDAR Semantic Segmentation146
A coarse-to-fine LiDAR and camera fusion-based network, named LIF-Seg, for LiDAR segmentation, which introduces a simple but effective early-fusion strategy to fully utilize the contextual information of images and can effectively address the weak spatiotemporal synchronization problem.
- Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-Based Perception145
This paper proposes a new framework for the outdoor LiDAR segmentation, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern while maintaining these inherent properties.
- SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds142
A novel 3D shape signature is proposed to explore the shape information from point clouds and is not only compact and effective but also robust to the noise, which serves as a soft constraint to improve the feature capability of multi-class discrimination.
- SCPNet: Semantic Scene Completion on Point Cloud135
A novel knowledge distillation objective is designed, dubbed Dense-to-Sparse Knowledge Distillation (DSKD), which transfers the dense, relation-based semantic knowledge from the multi-frame teacher to the single-frame student, significantly improving the representation learning of thesingle-frame model.
- Dependency Exploitation: A Unified CNN-RNN Approach for Visual Emotion Recognition135
This paper proposes a unified CNNRNN model to predict the emotion based on the fused features from different levels by exploiting the dependency among them and leverages convolutional neural network with multiple layers to extract different levels of features within a multi-task learning framework.
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- LiDAR-based Panoptic Segmentation via Dynamic Shifting Network121
This work proposes the Dynamic Shifting Network (DS-Net), which serves as an effective panoptic segmentation framework in the point cloud realm and presents an efficient learnable clustering module, dynamic shifting, which adapts kernel functions on-the-fly for different instances.
- GANet: Goal Area Network for Motion Forecasting117
A new goal area-based framework, named Goal Area Network (GANet), for motion forecasting, which models goal areas as preconditions for trajectory prediction, performing more robustly and accurately.
- UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase113
A unified multi-modal LiDAR segmentation network, termed UniSeg, is presented, which leverages the information of RGB images and three views of the point cloud, and accomplishes semantic segmentation and panoptic segmentation simultaneously.
- Penalizing Top Performers: Conservative Loss for Semantic Segmentation Adaptation107
This work proposes a novel loss function, i.e., Conservative Loss, which penalizes the extreme good and bad cases while encouraging the moderate examples and enables the network to learn features that are discriminative by gradient descent and are invariant to the change of domains via gradient ascend method.
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- AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching88
This work presents a novel domain-adaptive pipeline called AdaStereo that aims to align multi-level representations for deep stereo matching networks, and proposes a non-adversarial progressive color transfer algorithm for input image-level alignment.
- Tensor Low-Rank Reconstruction for Semantic Segmentation76
This paper proposes a new approach to model the 3D context representations, which not only avoids the space compression but also tackles the high-rank difficulty, and achieves state-of-the-art on various public datasets.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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