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Danping Zou

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13 papers

2 author rows

AAAI Conference 2026 Conference Paper

CoordAR: One-Reference 6D Pose Estimation of Novel Objects via Autoregressive Coordinate Map Generation

Dexin Zuo
Ang Li
Wei Wang
Wenxian Yu
Danping Zou

Object 6D pose estimation, a crucial task for robotics and augmented reality applications, becomes particularly challenging when dealing with novel objects whose 3D models are not readily available. To reduce dependency on 3D models, recent studies have explored one-reference-based pose estimation, which requires only a single reference view instead of a complete 3D model. However, existing methods that rely on real-valued coordinate regression suffer from limited global consistency due to the local nature of convolutional architectures and face challenges in symmetric or occluded scenarios owing to a lack of uncertainty modeling. We present CoordAR, a novel autoregressive framework for one-reference 6D pose estimation of unseen objects. CoordAR formulates 3D-3D correspondences between the reference and query views as a map of discrete tokens, which is obtained in an autoregressive and probabilistic manner. To enable accurate correspondence regression, CoordAR introduces 1) a novel coordinate map tokenization that enables probabilistic prediction over discretized 3D space; 2) a modality-decoupled encoding strategy that separately encodes RGB appearance and coordinate cues; and 3) an autoregressive transformer decoder conditioned on both position-aligned query features and the partially generated token sequence. With these novel mechanisms, CoordAR significantly outperforms existing methods on multiple benchmarks and demonstrates strong robustness to symmetry, occlusion, and other challenges in real-world tests.

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AAAI Conference 2026 Conference Paper

Head-Aware KV Cache Compression for Efficient Visual Autoregressive Modeling

Ziran Qin
Youru Lv
Mingbao Lin
Hang Guo
Zeren Zhang
Danping Zou
Weiyao Lin

Visual Autoregressive (VAR) models adopt a next-scale prediction paradigm, offering high-quality content generation with substantially fewer decoding steps. However, existing VAR models suffer from significant attention complexity and severe memory overhead due to the accumulation of key-value (KV) caches across scales. In this paper, we tackle this challenge by introducing KV cache compression into the next-scale generation paradigm. We begin with a crucial observation: attention heads in VAR models can be divided into two functionally distinct categories: Contextual Heads focus on maintaining semantic consistency, while Structural Heads are responsible for preserving spatial coherence. This structural divergence causes existing one-size-fits-all compression methods to perform poorly on VAR models. To address this, we propose HACK, a training-free Head-Aware KV cache Compression frameworK. HACK utilizes an offline classification scheme to separate head types, enabling it to apply pattern-specific compression strategies with asymmetric cache budgets for each category. By doing so, HACK effectively constrains the average KV cache length within a fixed budget B, reducing the theoretical attention complexity from O(n4) to O(Bn2). Extensive experiments on multiple VAR models across text-to-image and class-conditional tasks validate the effectiveness and generalizability of HACK. It achieves up to 70% KV cache compression without degrading output quality, resulting in memory savings and faster in- ference. For example, HACK provides a 1.75× memory reduction and a 1.57× speedup on Infinity-8B.

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IROS Conference 2025 Conference Paper

Mapless Collision-Free Flight via MPC using Dual KD-Trees in Cluttered Environments

Linzuo Zhang
Yu Hu 0019
Yang Deng
Feng Yu 0024
Danping Zou

Collision-free flight in cluttered environments is a critical capability for autonomous quadrotors. Traditional methods often rely on detailed 3D map construction, trajectory generation, and tracking. However, this cascade pipeline can introduce accumulated errors and computational delays, limiting flight agility and safety. In this paper, we propose a novel method for enabling collision-free flight in cluttered environments without explicitly constructing 3D maps or generating and tracking collision-free trajectories. Instead, we leverage Model Predictive Control (MPC) to directly produce safe actions from sparse waypoints and point clouds from a depth camera. These sparse waypoints are dynamically adjusted online based on nearby obstacles detected from point clouds. To achieve this, we introduce a dual KD-Tree mechanism: the Obstacle KD-Tree quickly identifies the nearest obstacle for avoidance, while the Edge KD-Tree provides a robust initial guess for the MPC solver, preventing it from getting stuck in local minima during obstacle avoidance. We validate our approach through extensive simulations and real-world experiments. The results show that our approach significantly outperforms the mapping-based methods and is also superior to imitation learning-based methods, demonstrating reliable obstacle avoidance at up to 12 m/s in simulations and 6 m/s in real-world tests. Our method provides a simple and robust alternative to existing methods. The code is publicly available at https://github.com/SJTU-ViSYS-team/avoid-mpc.

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NeurIPS Conference 2025 Conference Paper

PlanarGS: High-Fidelity Indoor 3D Gaussian Splatting Guided by Vision-Language Planar Priors

Xirui Jin
Renbiao Jin
Boying Li
Danping Zou
Wenxian Yu

Three-dimensional Gaussian Splatting (3DGS) has recently emerged as an efficient representation for novel-view synthesis, achieving impressive visual quality. However, in scenes dominated by large and low-texture regions, common in indoor environments, the photometric loss used to optimize 3DGS yields ambiguous geometry and fails to recover high-fidelity 3D surfaces. To overcome this limitation, we introduce PlanarGS, a 3DGS-based framework tailored for indoor scene reconstruction. Specifically, we design a pipeline for Language-Prompted Planar Priors (LP3) that employs a pretrained vision-language segmentation model and refines its region proposals via cross-view fusion and inspection with geometric priors. 3D Gaussians in our framework are optimized with two additional terms: a planar prior supervision term that enforces planar consistency, and a geometric prior supervision term that steers the Gaussians toward the depth and normal cues. We have conducted extensive experiments on standard indoor benchmarks. The results show that PlanarGS reconstructs accurate and detailed 3D surfaces, consistently outperforming state-of-the-art methods by a large margin. Project page: https: //planargs. github. io

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ICRA Conference 2025 Conference Paper

ThermoStereoRT: Thermal Stereo Matching in Real Time via Knowledge Distillation and Attention-Based Refinement

Anning Hu
Ang Li 0029
Xirui Jin
Danping Zou

We introduce ThermoStereoRT, a real-time thermal stereo matching method designed for all-weather conditions that recovers disparity from two rectified thermal stereo images, envisioning applications such as night-time drone surveillance or under-bed cleaning robots. Leveraging a lightweight yet powerful backbone, ThermoStereoRT constructs a 3D cost volume from thermal images and employs multi-scale attention mechanisms to produce an initial disparity map. To refine this map, we design a novel channel and spatial attention module. Addressing the challenge of sparse ground truth data in thermal imagery, we utilize knowledge distillation to boost performance without increasing computational demands. Comprehensive evaluations on multiple datasets demonstrate that ThermoStereoRT delivers both real-time capacity and robust accuracy, making it a promising solution for real-world deployment in various challenging environments. Our code will be released on https://github.com/SJTU-ViSYS-team/ThermoStereoRT.

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ICRA Conference 2025 Conference Paper

VisFly: An Efficient and Versatile Simulator for Training Vision-Based Flight

Fanxing Li
Fangyu Sun
Tianbao Zhang
Danping Zou

We present VisFly, a quadrotor simulator designed to efficiently train vision-based flight policies using reinforcement learning algorithms. VisFly offers a user-friendly framework and interfaces, leveraging Habitat-Sim's rendering engines to achieve frame rates exceeding 10, 000 frames per second for rendering motion and sensor data. The simulator incorporates differentiable physics and is seamlessly wrapped with the Gym environment, facilitating the straightforward implementation of various learning algorithms. It supports the directly importing open-source scene datasets compatible with Habitat-Sim, enabling training on diverse real-world environments simultaneously. To validate our simulator, we also make three reinforcement learning examples for typical flight tasks relying on visual observations. The simulator is now available at [https://github.com/SJTU-ViSYS-team/VisFly].

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ICRA Conference 2024 Conference Paper

Ground-Fusion: A Low-cost Ground SLAM System Robust to Corner Cases

Jie Yin
Ang Li
Wei Xi
Wenxian Yu
Danping Zou

We introduce Ground-Fusion, a low-cost sensor fusion simultaneous localization and mapping (SLAM) system for ground vehicles. Our system features efficient initialization, effective sensor anomaly detection and handling, real-time dense color mapping, and robust localization in diverse environments. We tightly integrate RGB-D images, inertial measurements, wheel odometer and GNSS signals within a factor graph to achieve accurate and reliable localization both indoors and outdoors. To ensure successful initialization, we propose an efficient strategy that comprises three different methods: stationary, visual, and dynamic, tailored to handle diverse cases. Furthermore, we develop mechanisms to detect sensor anomalies and degradation, handling them adeptly to maintain system accuracy. Our experimental results on both public and self-collected datasets demonstrate that Ground-Fusion outperforms existing low-cost SLAM systems in corner cases. We release the code and datasets at https://github.com/SJTU-ViSYS/Ground-Fusion.

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ICRA Conference 2024 Conference Paper

Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth Conversion

Ang Li
Anning Hu
Wei Xi
Wenxian Yu
Danping Zou

Accurate and dense depth estimation with stereo cameras and LiDAR is an important task for automatic driving and robotic perception. While sparse hints from LiDAR points have improved cost aggregation in stereo matching, their effectiveness is limited by the low density and non-uniform distribution. To address this issue, we propose a novel stereo-LiDAR depth estimation network with Semi-Dense hint Guidance, named SDG-Depth. Our network includes a deformable propagation module for generating a semi-dense hint map and a confidence map by propagating sparse hints using a learned deformable window. These maps then guide cost aggregation in stereo matching. To reduce the triangulation error in depth recovery from disparity, especially in distant regions, we introduce a disparity-depth conversion module. Our method is both accurate and efficient. The experimental results on benchmark tests show its superior performance. Our code is available at https://github.com/SJTU-ViSYS/SDG-Depth.

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EAAI Journal 2024 Journal Article

Transformer framework for depth-assisted UDA semantic segmentation

Yunna Song
Jinlong Shi
Danping Zou
Caisheng Liu
Suqin Bai
Xin Shu
Qiang Qian
Dan Xu

Unsupervised domain adaptation (UDA) plays a crucial role in transferring models trained on synthetic datasets to real-world datasets. In semantic segmentation, UDA can alleviate the requirement of a large number of dense semantic annotations. Some UDA semantic segmentation approaches have already leveraged depth information to enhance semantic features for improved segmentation accuracy. Building on this, we introduce a UDA multitask Transformer framework called Multi-former. Multi-former contains a semantic-segmentation and a depth-estimation network. Depth-estimation network extracts more informative depth features to estimate depth and assist in semantic segmentation. In addition, considering the issue of imbalanced class pixel distributions in the source domain, we present a rare class mix strategy (RCM) to balance domain adaptability for all classes. To further enhance the UDA semantic segmentation performance, we design a mixed label loss weight strategy (MLW), which employs different types of weights to comprehensively utilize the features of pseudo-label. Experimental results demonstrate the effectiveness of the proposed approach, which achieves the best mean intersection over union (mIoU) of 56. 1% and 76. 3% on the two UDA benchmark tasks of synthetic datasets to real-world datasets, respectively. The code and models are available at https: //github. com/fz-ss/Multi-former.

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IROS Conference 2021 Conference Paper

Robust Initialization of Multi-camera SLAM with Limited View Overlaps and Inaccurate Extrinsic Calibration

Ang Li 0029
Danping Zou
Wenxian Yu

This paper proposes a robust initialization method for a multi-camera visual SLAM system where cameras have only a limited common field of views and inaccurate extrinsic calibration. The limited common field of views leads to only a few common features that can be matched between cameras. Inaccurate extrinsic poses, caused by vibrations or misplacement of cameras after offline calibration, make it even harder for triangulating the seed 3D points to initialize the SLAM system successfully. Instead of taking the extrinsic parameters as constants for feature matching and 3D point triangulation as most multi-camera systems did, we propose to take the inaccurate extrinsic poses as soft constraints to accommodate the calibration errors. Our initialization method consists of two stages by matching across different cameras and between two key frames. Both stages involve optimizing the cost functions that contain the extrinsic pose priors from inaccurate calibration parameters. By incorporating those soft pose constraints, we may avoid false feature matching and triangulation caused by inaccurate extrinsic parameters, while keeping solution space limited when only a few feature correspondences exist. The results in real-world tests show that such a simple solution can improve the success rate of SLAM initialization notably, even when the pose priors from the offline calibration differ significantly from the real ones.

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ICRA Conference 2020 Conference Paper

TextSLAM: Visual SLAM with Planar Text Features

Boying Li
Danping Zou
Daniele Sartori
Ling Pei
Wenxian Yu

We propose to integrate text objects in man-made scenes tightly into the visual SLAM pipeline. The key idea of our novel text-based visual SLAM is to treat each detected text as a planar feature which is rich of textures and semantic meanings. The text feature is compactly represented by three parameters and integrated into visual SLAM by adopting the illumination-invariant photometric error. We also describe important details involved in implementing a full pipeline of text-based visual SLAM. To our best knowledge, this is the first visual SLAM method tightly coupled with the text features. We tested our method in both indoor and outdoor environments. The results show that with text features, the visual SLAM system becomes more robust and produces much more accurate 3D text maps that could be useful for navigation and scene understanding in robotic or augmented reality applications.

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ICRA Conference 2018 Conference Paper

Active Image-Based Modeling with a Toy Drone

Rui Huang 0001
Danping Zou
Richard Vaughan 0001
Ping Tan 0002

Image-based modeling techniques [1]–[3] can now generate photo-realistic 3D models from images. But it is up to users to provide high quality images with good coverage and view overlap, which makes the data capturing process tedious and time consuming. We seek to automate data capturing for image-based modeling. The core of our system is an iterative linear method to solve the multi-view stereo (MVS) problem quickly and plan the Next-Best-View (NBV) effectively. Our fast MVS algorithm enables online model reconstruction and quality assessment to determine the NBVs on the fly. We test our system with a toy unmanned aerial vehicle (UAV) in simulated, indoor and outdoor experiments. Results show that our system improves the efficiency of data acquisition and ensures the completeness of the final model.

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IROS Conference 2017 Conference Paper

An aerodynamic model-aided state estimator for multi-rotor UAVs

Rongzhi Wang
Danping Zou
Changqing Xu
Ling Pei
Peilin Liu
Wenxian Yu

A robust state estimator is presented by fusing the aerodynamic model of multi-rotor UAVs with measurements from optical flow and other low-cost sensors such as IMU, magnetometer, and ultrasonic sensor. Due to the particular aerodynamics of multi-rotor UAVs, the body velocity in the rotor plane is able to be measured by the accelerometer. We therefore propose a novel state estimator by fully exploring the characteristic of aerodynamics of multi-rotor UAV. Our state estimator is fast and easy to be implemented. We have tested our estimator with different platforms in different scenes. Experimental results show that our estimator performs robustly in low light conditions where existing methods usually fail.

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