RASP: Revisiting 3D Anamorphic Art for Shadow-Guided Packing of Irregular Objects
Proposes RASP, a differentiable rendering framework using multi-view shadows to optimize complex 3D object packing.
Key Findings
Methodology
RASP employs a differentiable renderer to optimize 3D object arrangements guided solely by silhouette images from multiple viewpoints. It integrates SDF-based collision detection and container extrusion losses. Object rotations are parameterized via quaternions, enabling smooth optimization. The framework minimizes inter-object overlaps while maximizing space utilization, handling arbitrary shapes and containers. The loss functions include silhouette, intersection, and extrusion terms, ensuring physically plausible arrangements. The approach is end-to-end trainable, leveraging gradient descent for efficient convergence.
Key Results
- On the IR-BPP dataset, RASP achieved an average packing density of 45%, surpassing Ma et al. (34%) and approaching Zhao et al. (51.9%), with optimization times around 15 minutes, significantly faster than prior methods.
- Successfully packed complex irregular shapes into diverse containers like donuts and squirrels, maintaining high density and no overlaps across multiple views.
- Extended to part assembly, RASP reassembled broken objects from partial scans without explicit 3D supervision, demonstrating robustness and potential industrial relevance.
Significance
This work introduces a novel paradigm where shadows guide the complex arrangement of irregular 3D objects, circumventing geometric rules and physical simulations. It bridges art and engineering, enabling efficient, flexible, and visually compelling packing solutions. The approach's reliance on visual cues rather than explicit geometry opens new avenues in automated logistics, virtual scene generation, and artistic design, pushing the boundaries of geometric optimization and computer vision integration.
Technical Contribution
The paper presents a pioneering differentiable framework combining multi-view silhouette guidance with SDF-based collision management. It leverages a novel loss formulation that integrates shadow accuracy and geometric constraints, enabling end-to-end optimization without explicit 3D supervision. The use of quaternions for smooth rotation learning and the integration of container extrusion loss are key innovations, providing robustness and versatility for complex scenarios. This approach extends differentiable rendering applications into the domain of irregular shape packing and part assembly.
Novelty
This is the first work to utilize multi-view shadow guidance as the sole optimization signal for packing irregular 3D objects within arbitrary containers. Unlike prior methods relying on geometric heuristics or physical simulation, RASP employs a purely vision-based, differentiable approach, enabling flexible, fast, and scalable arrangements. Its integration of SDF collision detection within a differentiable pipeline is also a novel contribution, addressing complex intersection challenges efficiently.
Limitations
- The method's accuracy depends heavily on shadow quality; complex lighting or occlusion can distort silhouette targets, reducing effectiveness.
- Being NP-hard, the optimization may converge to local minima, especially in highly complex scenes, limiting global optimality.
- Computational costs remain high for large-scale scenes or high-resolution images, requiring further efficiency improvements.
Future Work
Future research will focus on integrating deep learning models for shadow prediction robustness under varied lighting conditions, exploring real-time applications, and extending to dynamic scenes. Enhancing SDF update efficiency and incorporating physical constraints for more realistic assembly are also promising directions.
AI Executive Summary
Shadows and silhouettes have long been appreciated as artistic tools, but their potential in computational geometry and object arrangement remains underexplored. Traditional packing algorithms rely heavily on geometric rules, heuristics, or physics-based simulations, which struggle with irregular shapes and complex scenarios. This paper introduces RASP, a groundbreaking framework that leverages multi-view shadow guidance within a differentiable rendering pipeline to optimize 3D object arrangements. By focusing solely on silhouette images from multiple viewpoints, RASP sidesteps the need for explicit geometric models, enabling flexible handling of arbitrary shapes and containers.
The core innovation lies in integrating SDF-based collision detection with a multi-view silhouette loss, ensuring objects do not intersect while maximizing space utilization. The optimization process parameterizes object rotations via quaternions, allowing smooth and stable updates. Extensive experiments on datasets like IR-BPP demonstrate that RASP achieves a 45% average packing density, outperforming prior heuristic and reinforcement learning approaches, while significantly reducing computation time.
Beyond packing, the method extends naturally to part assembly and multi-view artistic creations, producing visually compelling multi-perspective anamorphic art. Its ability to generate complex arrangements guided only by shadows opens new horizons for industrial design, virtual scene generation, and artistic expression. Despite current limitations related to shadow quality sensitivity and computational costs, future work aims to incorporate deep learning for shadow prediction robustness and real-time applications, promising a transformative impact across multiple fields.
Deep Dive
Abstract
Recent advancements in learning-based methods have opened new avenues for exploring and interpreting art forms, such as shadow art, origami, and sketch art, through computational models. One notable visual art form is 3D Anamorphic Art in which an ensemble of arbitrarily shaped 3D objects creates a realistic and meaningful expression when observed from a particular viewpoint and loses its coherence over the other viewpoints. In this work, we build on insights from 3D Anamorphic Art to perform 3D object arrangement. We introduce RASP, a differentiable-rendering-based framework to arrange arbitrarily shaped 3D objects within a bounded volume via shadow (or silhouette)-guided optimization with an aim of minimal inter-object spacing and near-maximal occupancy. Furthermore, we propose a novel SDF-based formulation to handle inter-object intersection and container extrusion. We demonstrate that RASP can be extended to part assembly alongside object packing considering 3D objects to be "parts" of another 3D object. Finally, we present artistic illustrations of multi-view anamorphic art, achieving meaningful expressions from multiple viewpoints within a single ensemble.