Author name cluster

Yixi Cai

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

DeltaFlow: An Efficient Multi-frame Scene Flow Estimation Method

Qingwen Zhang
Xiaomeng Zhu
Yushan Zhang
Yixi Cai
Olov Andersson
Patric Jensfelt

Previous dominant methods for scene flow estimation focus mainly on input from two consecutive frames, neglecting valuable information in the temporal domain. While recent trends shift towards multi-frame reasoning, they suffer from rapidly escalating computational costs as the number of frames grows. To leverage temporal information more efficiently, we propose DeltaFlow ($\Delta$Flow), a lightweight 3D framework that captures motion cues via a $\Delta$ scheme, extracting temporal features with minimal computational cost, regardless of the number of frames. Additionally, scene flow estimation faces challenges such as imbalanced object class distributions and motion inconsistency. To tackle these issues, we introduce a Category-Balanced Loss to enhance learning across underrepresented classes and an Instance Consistency Loss to enforce coherent object motion, improving flow accuracy. Extensive evaluations on the Argoverse 2, Waymo and nuScenes datasets show that $\Delta$Flow achieves state-of-the-art performance with up to 22\% lower error and $2\times$ faster inference compared to the next-best multi-frame supervised method, while also demonstrating a strong cross-domain generalization ability. The code is open-sourced at https: //github. com/Kin-Zhang/DeltaFlow along with trained model weights.

PDF Details

IROS Conference 2025 Conference Paper

Efficient Swept Volume-Based Trajectory Generation for Arbitrary-Shaped Ground Robot Navigation

Yisheng Li
Longji Yin
Yixi Cai
Jianheng Liu
Fangcheng Zhu
Mingpu Ma
Siqi Liang 0004
Haotian Li

Navigating an arbitrary-shaped ground robot safely in cluttered environments remains a challenging problem. The existing trajectory planners that account for the robot’s physical geometry severely suffer from the intractable runtime. To achieve both computational efficiency and Continuous Collision Avoidance (CCA) of arbitrary-shaped ground robot planning, we proposed a novel coarse-to-fine navigation framework that significantly accelerates planning. In the first stage, a sampling-based method selectively generates distinct topological paths that guarantee a minimum inflated margin. In the second stage, a geometry-aware front-end strategy is designed to discretize these topologies into full-state robot motion sequences while concurrently partitioning the paths into SE(2) sub-problems and simpler ℝ 2 sub-problems for back-end optimization. In the final stage, an SVSDF-based optimizer generates trajectories tailored to these sub-problems and seamlessly splices them into a continuous final motion plan. Extensive benchmark comparisons show that the proposed method is one to several orders of magnitude faster than the cutting-edge methods in runtime while maintaining a high planning success rate and ensuring CCA.

Details

ICRA Conference 2025 Conference Paper

LVBA: LiDAR-Visual Bundle Adjustment for RGB Point Cloud Mapping

Rundong Li
Xiyuan Liu
Haotian Li
Zheng Liu 0019
Jiarong Lin
Yixi Cai
Fu Zhang 0002

Point cloud maps with accurate color are crucial in robotics and mapping applications. Existing approaches for producing RGB-colorized maps are primarily based on realtime localization using filter-based estimation or sliding window optimization, which may lack accuracy and global consistency. In this work, we introduce a novel global LiDAR-Visual bundle adjustment (BA) named LVBA to improve the quality of RGB point cloud mapping beyond existing baselines. LVBA first optimizes LiDAR poses via a global LiDAR BA, followed by a photometric visual BA incorporating planar features from the LiDAR point cloud for camera pose optimization. Additionally, to address the challenge of map point occlusions in constructing optimization problems, we implement a novel LiDAR-assisted global visibility algorithm in LVBA. To evaluate the effectiveness of LVBA, we conducted extensive experiments by comparing its mapping quality against existing state-of-the-art baselines (i. e. , $\mathbf{R}^{3}$ LIVE and FAST-LIVO). Our results prove that LVBA can proficiently reconstruct high-fidelity, accurate RGB point cloud maps, outperforming its counterparts.

Details

ICRA Conference 2025 Conference Paper

Neural Surface Reconstruction and Rendering for LiDAR-Visual Systems

Jianheng Liu
Chunran Zheng
Yunfei Wan
Bowen Wang
Yixi Cai
Fu Zhang 0002

This paper presents a unified surface reconstruction and rendering framework for LiDAR-visual systems, integrating Neural Radiance Fields (NeRF) and Neural Distance Fields (NDF) to recover both appearance and structural information from posed images and point clouds. We address the structural visible gap between NeRF and NDF by utilizing a visible-aware occupancy map to classify space into the free, occupied, visible unknown, and background regions. This classification facilitates the recovery of a complete appearance and structure of the scene. We unify the training of the NDF and NeRF using a spatial-varying scale SDF-to-density transformation for levels of detail for both structure and appearance. The proposed method leverages the learned NDF for structure-aware NeRF training by an adaptive sphere tracing sampling strategy for accurate structure rendering. In return, NeRF further refines structural in recovering missing or fuzzy structures in the NDF. Extensive experiments demonstrate the superior quality and versatility of the proposed method across various scenarios. To benefit the community, the codes will be released at https://github.com/hku-mars/M2Mapping.

Details

IROS Conference 2024 Conference Paper

ROG-Map: An Efficient Robocentric Occupancy Grid Map for Large-scene and High-resolution LiDAR-based Motion Planning

Yunfan Ren
Yixi Cai
Fangcheng Zhu
Siqi Liang 0004
Fu Zhang 0002

Recent advances in LiDAR technology have opened up new possibilities for robotic navigation. Given the widespread use of occupancy grid maps (OGMs) in robotic motion planning, this paper aims to address the challenges of integrating LiDAR with OGMs. To this end, we propose ROG-Map, a uniform grid-based OGM that maintains a local map moving along with the robot to enable efficient map operation and reduce memory costs for large-scene autonomous flight. Moreover, we present a novel incremental obstacle inflation method that significantly reduces the computational cost of inflation. The proposed method outperforms state-of-the-art methods on various public datasets. To demonstrate the effectiveness and efficiency of ROG-Map, we integrate it into a complete quadrotor system and perform autonomous flights against both small obstacles and large-scale scenes. During real-world flight tests with a 0. 05 m resolution local map and 30 m×30 m×6 m local map size, ROG-Map takes only 29. 8 % of frame time on average to update the map at a frame rate of 50 Hz (i. e. , 5. 96 ms in 20 ms), including 0. 33 % (i. e. , 0. 66 ms) to perform obstacle inflation, which represents only half of the total map updating time when compared to the state-of-the-art baseline. We release ROG-Map as an open-source ROS package 1 to promote the development of LiDAR-based motion planning.

Details