Author name cluster

Cheng-Hao Kuo

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

1 author row

IROS Conference 2025 Conference Paper

Enhancing Single Image to 3D Generation using Gaussian Splatting and Hybrid Diffusion Priors

Hritam Basak
Hadi Tabatabaee
Shreekant Gayaka
Ming-Feng Li
Xin Yang
Cheng-Hao Kuo
Arnie Sen
Min Sun 0001

3D object generation from a single unposed RGB image is essential for robotic perception, as reconstructing complete geometry and texture is essential for precise manipulation, grasping, and scene understanding, which is key for autonomous navigation and dexterous interaction. Recent advancements in image-to-3D employ Gaussian Splatting with pre-trained 2D or 3D diffusion models, but a disparity exists: 2D models generate high-fidelity textures yet lack geometric consistency, while 3D models ensure structural coherence but produce overly smooth textures. To address this, we introduce a two-stage frequency-based distillation loss integrated with Gaussian Splatting, leveraging geometric priors from a 3D diffusion model’s low-frequency spectrum for structural consistency and a 2D diffusion model’s high-frequency details for sharper textures. Our approach achieves state-of-the-art 3D reconstruction quality, significantly improving robotic perception pipelines. Additionally, we demonstrate the easy adaptability of our method for highly accurate object pose estimation and tracking, which is critical for precise robotic grasping, manipulation, and scene understanding. Additional results can be found in the supplementary file.

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ICRA Conference 2025 Conference Paper

Modeling Uncertainty in 3D Gaussian Splatting Through Continuous Semantic Splatting

Joey Wilson
Marcelino Almeida
Min Sun 0001
Sachit Mahajan
Maani Ghaffari
Parker Ewen
Omid Ghasemalizadeh
Cheng-Hao Kuo

In this paper, we present a novel algorithm for probabilistically updating and rasterizing semantic maps within 3D Gaussian Splatting (3D-GS). Although previous methods have introduced algorithms which learn to rasterize features in 3D-GS for enhanced scene understanding, 3D-GS can fail without warning which presents a challenge for safety-critical robotic applications. To address this gap, we propose a method which advances the literature of continuous semantic mapping from voxels to ellipsoids, combining the precise structure of 3D-GS with the ability to quantify uncertainty of probabilistic robotic maps. Given a set of images, our algorithm performs a probabilistic semantic update directly on the 3D ellipsoids to obtain an expectation and variance through the use of conjugate priors. We also propose a probabilistic rasterization which returns per-pixel segmentation predictions with quantifiable uncertainty. We compare our method with similar probabilistic voxel-based methods to verify our extension to 3D ellipsoids, and perform ablation studies on uncertainty quantification and temporal smoothing.

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IROS Conference 2024 Conference Paper

PoCo: Point Context Cluster for RGBD Indoor Place Recognition

Jing Liang 0006
Zhuo Deng
Zheming Zhou
Omid Ghasemalizadeh
Dinesh Manocha
Min Sun 0001
Cheng-Hao Kuo
Arnie Sen

We present a novel end-to-end algorithm (PoCo) for the indoor RGB-D place recognition task, aimed at identifying the most likely match for a given query frame within a reference database. The task presents inherent challenges attributed to the constrained field of view and limited range of perception sensors. We propose a new network architecture, which generalizes the recent Context of Clusters (CoCs) to extract global descriptors directly from the noisy point clouds through end-to-end learning. Moreover, we develop the architecture by integrating both color and geometric modalities into the point features to enhance the global descriptor representation. We conducted evaluations on public datasets ScanNet-PR and ARKit with 807 and 5047 scenarios, respectively. PoCo achieves SOTA performance: on ScanNet-PR, we achieve R@1 of 64. 63%, a 5. 7% improvement from the best-published result CGis (61. 12%); on Arkit, we achieve R@1 of 45. 12%, a 13. 3% improvement from the best-published result CGis (39. 82%). In addition, PoCo shows higher efficiency than CGis in inference time (1. 75X-faster), and we demonstrate the effectiveness of PoCo in recognizing places within a real-world laboratory environment. Video: https://youtu.be/D8dObAeMiCw;

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ICRA Conference 2023 Conference Paper

Multimodal Neural Radiance Field

Haidong Zhu
Yuyin Sun
Chi Liu
Lu Xia
Jiajia Luo
Nan Qiao 0009
Ram Nevatia
Cheng-Hao Kuo

This paper addresses the challenge of reconstructing a scene with a neural radiance field (NeRF) for robot vision and scene understanding using multiple modalities. Researchers have introduced the use of NeRF to represent an object for synthesizing and rendering novel views of complex scenes by optimizing a 3-D radiance field for ray casting and rendering for 2-D RGB images. However, using RGB images alone introduces additional geometry ambiguities with transparent objects or complex scenes and cannot accurately depict the 3-D shapes. We discuss and solve this problem and use multiple modalities as input for the same NeRF model to build a multimodal NeRF by incorporating point clouds and infrared image supervision to prevent such bias. In contrast to RGB images, infrared images and point clouds are typically taken by separate cameras that cannot be aligned with the RGB camera. We further introduce the alignment of different modalities based on point cloud registration to estimate the relative transformation matrices between them before training a NeRF model with multiple modalities. We evaluate our model on chosen scenes from the ScanNet and M2DGR datasets and demonstrate that it outperforms existing state-of-the-art methods.

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IROS Conference 2022 Conference Paper

Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation

Shao-Yuan Lo
Wei Wang
Jim Thomas 0001
Jingjing Zheng
Vishal M. Patel
Cheng-Hao Kuo

Monocular depth estimation (MDE) has attracted intense study due to its low cost and critical functions for robotic tasks such as localization, mapping and obstacle detection. Supervised approaches have led to great success with the advance of deep learning, but they rely on large quantities of ground-truth depth annotations that are expensive to acquire. Unsupervised domain adaptation (UDA) transfers knowledge from labeled source data to unlabeled target data, so as to relax the constraint of supervised learning. However, existing UDA approaches may not completely align the domain gap across different datasets because of the domain shift problem. We believe better domain alignment can be achieved via well-designed feature decomposition. In this paper, we propose a novel UDA method for MDE, referred to as Learning Feature Decomposition for Adaptation (LFDA), which learns to decompose the feature space into content and style components. LFDA only attempts to align the content component since it has a smaller domain gap. Meanwhile, it excludes the style component which is specific to the source domain from training the primary task. Furthermore, LFDA uses separate feature distribution estimations to further bridge the domain gap. Extensive experiments on three domain adaptative MDE scenarios show that the proposed method achieves superior accuracy and lower computational cost compared to the state-of-the-art approaches.

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