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Deliberate Planning of 3D Bin Packing on Packing Configuration Trees [40.46267029657914]
Online 3D Bin Packing Problem (3D-BPP) has widespread applications in industrial automation. We propose to enhance the practical applicability of online 3D-BPP via learning on a novel hierarchical representation, packing configuration tree (PCT) PCT is a full-fledged description of the state and action space of bin packing which can support packing policy learning based on deep reinforcement learning (DRL)
arXiv Detail & Related papers (2025-04-06T09:07:10Z)
UniMamba: Unified Spatial-Channel Representation Learning with Group-Efficient Mamba for LiDAR-based 3D Object Detection [64.65405058535262]
Recent advances in LiDAR 3D detection have demonstrated the effectiveness of Transformer-based frameworks in capturing the global dependencies from point cloud spaces. Due to the considerable number of 3D voxels and quadratic complexity of Transformers, multiple sequences are grouped before feeding to Transformers, leading to a limited receptive field. Inspired by the impressive performance of State Space Models (SSM) achieved in the field of 2D vision tasks, we propose a novel Unified Mamba (UniMamba) Specifically, a UniMamba block is designed which mainly consists of locality modeling, Z-order serialization and local-global sequential aggregator.
arXiv Detail & Related papers (2025-03-15T06:22:31Z)
Neural Packing: from Visual Sensing to Reinforcement Learning [24.35678534893451]
We present a novel learning framework to solve the transport-and-packing (TAP) problem in 3D. It constitutes a full solution pipeline from partial observations of input objects via RGBD sensing and recognition to final box placement, via robotic motion planning, to arrive at a compact packing in a target container.
arXiv Detail & Related papers (2023-10-17T02:42:54Z)
Convolutional Occupancy Models for Dense Packing of Complex, Novel Objects [75.54599721349037]
We present a fully-convolutional shape completion model, F-CON, that can be easily combined with off-the-shelf planning methods for dense packing in the real world. We also release a simulated dataset, COB-3D-v2, that can be used to train shape completion models for real-word robotics applications. Finally, we equip a real-world pick-and-place system with F-CON, and demonstrate dense packing of complex, unseen objects in cluttered scenes.
arXiv Detail & Related papers (2023-07-31T19:08:16Z)
Geometric-aware Pretraining for Vision-centric 3D Object Detection [77.7979088689944]
We propose a novel geometric-aware pretraining framework called GAPretrain. GAPretrain serves as a plug-and-play solution that can be flexibly applied to multiple state-of-the-art detectors. We achieve 46.2 mAP and 55.5 NDS on the nuScenes val set using the BEVFormer method, with a gain of 2.7 and 2.1 points, respectively.
arXiv Detail & Related papers (2023-04-06T14:33:05Z)
Hierarchical Point Attention for Indoor 3D Object Detection [111.04397308495618]
This work proposes two novel attention operations as generic hierarchical designs for point-based transformer detectors. First, we propose Multi-Scale Attention (MS-A) that builds multi-scale tokens from a single-scale input feature to enable more fine-grained feature learning. Second, we propose Size-Adaptive Local Attention (Local-A) with adaptive attention regions for localized feature aggregation within bounding box proposals.
arXiv Detail & Related papers (2023-01-06T18:52:12Z)
Planning Irregular Object Packing via Hierarchical Reinforcement Learning [85.64313062912491]
We propose a deep hierarchical reinforcement learning approach to plan packing sequence and placement for irregular objects. We show that our approach can pack more objects with less time cost than the state-of-the-art packing methods of irregular objects.
arXiv Detail & Related papers (2022-11-17T07:16:37Z)
Online 3D Bin Packing Reinforcement Learning Solution with Buffer [1.8060107352742993]
We present a new reinforcement learning framework for a 3D-BPP solution for improving performance. We implement a model-based RL method adapted from the popular algorithm AlphaGo. Our adaptation is capable of working in single-player and score based environments.
arXiv Detail & Related papers (2022-08-15T11:28:20Z)
Learning Practically Feasible Policies for Online 3D Bin Packing [36.33774915391967]
We tackle the Online 3D Bin Packing Problem, a challenging yet practically useful variant of the classical Bin Packing Problem. Online 3D-BPP can be naturally formulated as Markov Decision Process (MDP) We adopt deep reinforcement learning, in particular, the on-policy actor-critic framework, to solve this MDP with constrained action space.
arXiv Detail & Related papers (2021-08-31T08:37:58Z)
A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing [7.79020719611004]
We propose a Deep Reinforcement Learning (Deep RL) algorithm for solving the online 3D bin packing problem. The focus is on producing decisions that can be physically implemented by a robotic loading arm. We show that the RL-based method outperforms state-of-the-art online bin packings in terms of empirical competitive ratio and volume efficiency.
arXiv Detail & Related papers (2020-07-01T13:02:04Z)
GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Pixel Labeling [92.90448357454274]
We propose the Gated Scale-Transfer Operation (GSTO) to properly transit spatial-supervised features to another scale. By plugging GSTO into HRNet, we get a more powerful backbone for pixel labeling. Experiment results demonstrate that GSTO can also significantly boost the performance of multi-scale feature aggregation modules.
arXiv Detail & Related papers (2020-05-27T13:46:58Z)

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