arXiv subjects

Chengkai Dai

Publications and source records attributed to Chengkai Dai.

12 recordsLinked to original sources

arXiv2026

WildFab: Multi-Axis 3D Printing from Models in the Wild

Multi-axis 3D printing enables support-free fabrication and improved part quality, but robustly processing real-world geometries remains challenging. Models from design workflows or direct data acquisition often contain solid--shell combinations and non-manifold structures. Handling such models in the wild typically requires time-consuming geometry repair, which may alter the intended geometry. In this work, we present WildFab, a computational framework for multi-axis 3D printing that directly computes spatial toolpath and global collision-free motion from input models. Our pipeline builds on a hybrid query representation that combines a neural unsigned distance field (UDF) with a regularized generalized winding number field (reg-GWN). The UDF supplies differentiable surface-distance and direction queries, while the reg-GWN resolves near-surface ambiguity in the fitted UDF by providing reliable surface localization and a solid-void indicator. Based on this representation, we introduce a high-precision spatial toolpath computation algorithm that iteratively projects points between optimized guidance-field level sets and reg-GWN gradient-magnitude ridges. Subsequently, we develop an efficient and robust coarse-to-fine collision checking scheme for motion planning: UDF-based rejection first identifies potential collisions, while time-varying reg-GWN verification accurately resolves collision pairs for both solid and shell components. We validate WildFab on diverse inputs, demonstrating successful computation from non-manifold parametric surfaces, voxelized topology-optimization results, implicit models, raw scanned point clouds, and non-watertight meshes. The fabrication results highlight our method's ability to advance end-to-end design-to-3DP workflows.

cs.GR↗

arXiv2026

Proximity3D: Shape from Capacitive Proximity on Sensing Manifold

Most shape reconstruction methods assume measurements defined over planar sensing domains, such as RGB images or depth maps. In this paper, we use a curved capacitive textile as a shape sensor, treating its surface as a non-planar sensing manifold. Each scan is represented as a capacitive proximity field on this manifold, induced by the interaction between the curved electrode layout and nearby object geometry. We introduce a multi-view feedforward reconstruction model that aggregates these fields across known sensor views and recovers the observed object shape. Simulated and physical experiments demonstrate robust reconstruction from capacitive proximity signals acquired on curved sensing surfaces, pointing toward a new route to robotic near-field geometric awareness via embodied sensing.

cs.CV↗

arXiv2026

Trajectory Optimization for Collision-Aware Redundant Robotic Multi-Axis Additive Manufacturing by Constrained Gradient Projection

Redundant robotic multi-axis additive manufacturing (MAAM) enables support-free and conformal fabrication, but trajectory optimization for long-horizon paths remains challenging under strict deposition-position constraints and time-varying collision constraints. This work proposes a computational framework for collision-aware trajectory optimization in redundant robotic MAAM. We first formulate nozzle-workpiece relative kinematics using a relative Jacobian, and develop a differentiable SDF-based collision model that captures fabrication-induced geometry evolution and provides optimization gradients. The deposition position is then enforced as a hard waypoint-wise equality constraint through iterative projection onto the self-motion manifold, with the loss gradient restricted to the corresponding tangent space. Experiments on an 8-DOF robotic MAAM platform with diverse long-horizon support-free and conformal toolpaths show that our method maintains a mean nozzle-position error below 10{\mu}m, reduces maximum joint jerk by up to $77.6\%$, and eliminates all sampled collision and orientation violations. Compared with the SQP-based baseline, it achieves up to a 10.2x speedup and improved convergence. Physical fabrication experiments further verify that the resulting smooth, collision-free trajectories enable successful printing of complex geometries with fewer visible deposition artifacts.

cs.RO↗

arXiv2025

From Prompts to Printable Models: Support-Effective 3D Generation via Offset Direct Preference Optimization

Current text-to-3D models prioritize visual fidelity but often neglect physical fabricability, resulting in geometries requiring excessive support structures. This paper introduces SEG (\textit{\underline{S}upport-\underline{E}ffective \underline{G}eneration}), a novel framework that integrates Direct Preference Optimization with an Offset (ODPO) into the 3D generation pipeline to directly optimize models for minimal support material usage. By incorporating support structure simulation into the training process, SEG encourages the generation of geometries that inherently require fewer supports, thus reducing material waste and production time. We demonstrate SEG's effectiveness through extensive experiments on two benchmark datasets, Thingi10k-Val and GPT-3DP-Val, showing that SEG significantly outperforms baseline models such as TRELLIS, DPO, and DRO in terms of support volume reduction and printability. Qualitative results further reveal that SEG maintains high fidelity to input prompts while minimizing the need for support structures. Our findings highlight the potential of SEG to transform 3D printing by directly optimizing models during the generative process, paving the way for more sustainable and efficient digital fabrication practices.

cs.RO↗

arXiv2025

Towards Intuitive Human-Robot Interaction through Embodied Gesture-Driven Control with Woven Tactile Skins

This paper presents a novel human-robot interaction (HRI) framework that enables intuitive gesture-driven control through a capacitance-based woven tactile skin. Unlike conventional interfaces that rely on panels or handheld devices, the woven tactile skin integrates seamlessly with curved robot surfaces, enabling embodied interaction and narrowing the gap between human intent and robot response. Its woven design combines fabric-like flexibility with structural stability and dense multi-channel sensing through the interlaced conductive threads. Building on this capability, we define a gesture-action mapping of 14 single- and multi-touch gestures that cover representative robot commands, including task-space motion and auxiliary functions. A lightweight convolution-transformer model designed for gesture recognition in real time achieves an accuracy of near-100%, outperforming prior baseline approaches. Experiments on robot arm tasks, including pick-and-place and pouring, demonstrate that our system reduces task completion time by up to 57% compared with keyboard panels and teach pendants. Overall, our proposed framework demonstrates a practical pathway toward more natural and efficient embodied HRI.

cs.RO↗

arXiv2025

INF-3DP: Implicit Neural Fields for Collision-Free Multi-Axis 3D Printing

We introduce a general, scalable computational framework for multi-axis 3D printing based on implicit neural fields (INFs) that unifies all stages of toolpath generation and global collision-free motion planning. In our pipeline, input models are represented as signed distance fields, with fabrication objectives such as support-free printing, surface finish quality, and extrusion control being directly encoded in the optimization of an implicit guidance field. This unified approach enables toolpath optimization across both surface and interior domains, allowing shell and infill paths to be generated via implicit field interpolation. The printing sequence and multi-axis motion are then jointly optimized over a continuous quaternion field. Our continuous formulation constructs the evolving printing object as a time-varying SDF, supporting differentiable global collision handling throughout INF-based motion planning. Compared to explicit-representation-based methods, INF-3DP achieves up to two orders of magnitude speedup and significantly reduces waypoint-to-surface error. We validate our framework on diverse, complex models and demonstrate its efficiency with physical fabrication experiments using a robot-assisted multi-axis system.

cs.RO↗

arXiv2025

Curve-based slicer for multi-axis DLP 3D printing

This paper introduces a novel curve-based slicing method for generating planar layers with dynamically varying orientations in digital light processing (DLP) 3D printing. Our approach effectively addresses key challenges in DLP printing, such as regions with large overhangs and staircase artifacts, while preserving its intrinsic advantages of high resolution and fast printing speeds. We formulate the slicing problem as an optimization task, in which parametric curves are computed to define both the slicing layers and the model partitioning through their tangent planes. These curves inherently define motion trajectories for the build platform and can be optimized to meet critical manufacturing objectives, including collision-free motion and floating-free deposition. We validate our method through physical experiments on a robotic multi-axis DLP printing setup, demonstrating that the optimized curves can robustly guide smooth, high-quality fabrication of complex geometries.

cs.GR↗

arXiv2024

Computer-Controlled 3D Freeform Surface Weaving

In this paper, we present a new computer-controlled weaving technology that enables the fabrication of woven structures in the shape of given 3D surfaces by using threads in non-traditional materials with high bending-stiffness, allowing for multiple applications with the resultant woven fabrics. A new weaving machine and a new manufacturing process are developed to realize the function of 3D surface weaving by the principle of short-row shaping. A computational solution is investigated to convert input 3D freeform surfaces into the corresponding weaving operations (indicated as W-code) to guide the operation of this system. A variety of examples using cotton threads, conductive threads and optical fibres are fabricated by our prototype system to demonstrate its functionality.

cs.GR↗

arXiv2020

Fast Generation of High Fidelity RGB-D Images by Deep-Learning with Adaptive Convolution

Using the raw data from consumer-level RGB-D cameras as input, we propose a deep-learning based approach to efficiently generate RGB-D images with completed information in high resolution. To process the input images in low resolution with missing regions, new operators for adaptive convolution are introduced in our deep-learning network that consists of three cascaded modules -- the completion module, the refinement module and the super-resolution module. The completion module is based on an architecture of encoder-decoder, where the features of input raw RGB-D will be automatically extracted by the encoding layers of a deep neural-network. The decoding layers are applied to reconstruct the completed depth map, which is followed by a refinement module to sharpen the boundary of different regions. For the super-resolution module, we generate RGB-D images in high resolution by multiple layers for feature extraction and a layer for up-sampling. Benefited from the adaptive convolution operators newly proposed in this paper, our results outperform the existing deep-learning based approaches for RGB-D image complete and super-resolution. As an end-to-end approach, high fidelity RGB-D images can be generated efficiently at the rate of around 21 frames per second.

cs.CV↗

arXiv2019

Planning Jerk-Optimized Trajectory with Discrete-Time Constraints for Redundant Robots

We present a method for effectively planning the motion trajectory of robots in manufacturing tasks, the tool-paths of which are usually complex and have a large number of discrete-time constraints as waypoints. Kinematic redundancy also exists in these robotic systems. The jerk of motion is optimized in our trajectory planning method at the meanwhile of fabrication process to improve the quality of fabrication.

cs.RO↗

arXiv2019

General Support-Effective Decomposition for Multi-Directional 3D Printing

We present a method for fabricating general models with multi-directional 3D printing systems by printing different model regions along with different directions. The core of our method is a support-effective volume decomposition algorithm that minimizes the area of the regions with large overhangs. A beam-guided searching algorithm with manufacturing constraints determines the optimal volume decomposition, which is represented by a sequence of clipping planes. While current approaches require manually assembling separate components into a final model, our algorithm allows for directly printing the final model in a single pass. It can also be applied to models with loops and handles. A supplementary algorithm generates special supporting structures for models where supporting structures for large overhangs cannot be eliminated. We verify the effectiveness of our method using two hardware systems: a Cartesian-motion based system and an angular-motion based system. A variety of 3D models have been successfully fabricated on these systems.

cs.RO↗

arXiv2016

Motion Imitation Based on Sparsely Sampled Correspondence

Existing techniques for motion imitation often suffer a certain level of latency due to their computational overhead or a large set of correspondence samples to search. To achieve real-time imitation with small latency, we present a framework in this paper to reconstruct motion on humanoids based on sparsely sampled correspondence. The imitation problem is formulated as finding the projection of a point from the configuration space of a human's poses into the configuration space of a humanoid. An optimal projection is defined as the one that minimizes a back-projected deviation among a group of candidates, which can be determined in a very efficient way. Benefited from this formulation, effective projections can be obtained by using sparse correspondence. Methods for generating these sparse correspondence samples have also been introduced. Our method is evaluated by applying the human's motion captured by a RGB-D sensor to a humanoid in real-time. Continuous motion can be realized and used in the example application of tele-operation.

cs.RO↗