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20212026
most citedSSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving

12 citations · 22 across the 12 of their papers we have counts for

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cs.CV2026

Spatial4D-Bench: A Versatile 4D Spatial Intelligence Benchmark

Pan Wang, Yang Liu, Guile Wu +23

4D spatial intelligence involves perceiving and processing how objects move or change over time. Humans naturally possess 4D spatial intelligence, supporting a broad spectrum of sp…

cs.CV2025

DCDB: Dynamic Conditional Dual Diffusion Bridge for Ill-posed Multi-Tasks

Chengjie Huang, Jiafeng Yan, Jing Li +1

Conditional diffusion models have made impressive progress in the field of image processing, but the characteristics of constructing data distribution pathways make it difficult to…

cs.CV2025

How Hard Is Snow? A Paired Domain Adaptation Dataset for Clear and Snowy Weather: CADC+

Mei Qi Tang, Sean Sedwards, Chengjie Huang +1

The impact of snowfall on 3D object detection performance remains underexplored. Conducting such an evaluation requires a dataset with sufficient labelled data from both weather co…

cs.CV2025

MFSeg: Efficient Multi-frame 3D Semantic Segmentation

Chengjie Huang, Krzysztof Czarnecki

We propose MFSeg, an efficient multi-frame 3D semantic segmentation framework. By aggregating point cloud sequences at the feature level and regularizing the feature extraction and…

cs.CV2024

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation

Chengjie Huang, Vahdat Abdelzad, Sean Sedwards +1

Input aggregation is a simple technique used by state-of-the-art LiDAR 3D object detectors to improve detection. However, increasing aggregation is known to have diminishing return…

cs.CV2024

SOAP: Cross-sensor Domain Adaptation for 3D Object Detection Using Stationary Object Aggregation Pseudo-labelling

Chengjie Huang, Vahdat Abdelzad, Sean Sedwards +1

We consider the problem of cross-sensor domain adaptation in the context of LiDAR-based 3D object detection and propose Stationary Object Aggregation Pseudo-labelling (SOAP) to gen…