Seeing Before Generating: Object Perception Enhances Single-View 3D Reconstruction
This paper introduces a perception-guided framework for single-view 3D reconstruction, achieving up to 32.7% reduction in Chamfer distance and significant metric improvements.
Key Findings
Methodology
The proposed approach integrates pretrained perception models—such as vision-language models (VLMs) and depth estimators—into the 3D reconstruction pipeline via a generation-perception alignment module (GPAM). This module aligns perceptual features with the generative network’s intermediate features, guiding the diffusion-based multi-view image synthesis process. The framework is model-agnostic and employs perceptual signals—semantic descriptions and depth features—to improve structural and semantic fidelity. During training, GPAM is optimized to minimize perceptual loss functions like cosine similarity and CKA, enabling effective feature alignment. During inference, the perceptual guidance steers the diffusion process, resulting in more accurate and semantically consistent 3D reconstructions.
Key Results
- On Objaverse, the combined semantic and depth perception guidance reduced Chamfer distance by 9.0% for Wonder3D and 32.7% for Era3D, with IoU reaching 0.558 and PSNR surpassing 22.05. These improvements were statistically significant, demonstrating the robustness of perception-guided reconstruction.
- Single-modality perception signals outperformed baseline models, and their combination yielded the best results, especially on complex objects with intricate details, confirming the complementary nature of semantic and geometric cues.
- Ablation studies showed that the CKA loss and multi-modal fusion significantly enhanced the quality, with the perceptual signals helping recover fine details and structural accuracy.
Significance
This work advances 3D reconstruction by systematically incorporating rich perceptual knowledge, addressing the limitations of purely geometric or prior-based methods. It demonstrates that pretrained perception models can serve as external knowledge sources, improving semantic consistency and structural plausibility. The approach bridges the gap between perception and generation, opening new avenues for robust, real-time 3D scene understanding and modeling, with broad implications for AR/VR, robotics, and content creation.
Technical Contribution
The paper introduces a flexible, model-agnostic framework that leverages pretrained perception models for guiding diffusion-based multi-view synthesis. Key innovations include the design of the GPAM, multi-modal perceptual feature extraction, and a perceptual loss-based alignment mechanism. This approach enables explicit semantic and geometric guidance during generation, surpassing prior methods that rely solely on implicit priors or geometric constraints. The framework’s modularity allows easy integration into existing pipelines, facilitating broader adoption.
Novelty
This is the first comprehensive attempt to embed multi-modal pretrained perception signals—semantic descriptions via language models and depth features—into single-view 3D reconstruction. The generation-perception alignment mechanism ensures perceptual consistency, addressing the common issues of semantic inconsistency and structural implausibility in prior works. Unlike previous methods that depend on geometric priors or large-scale generative models alone, this work explicitly incorporates rich perceptual knowledge, marking a significant step forward.
Limitations
- The effectiveness depends heavily on the quality of pretrained perception models; poor perception performance in certain categories may limit reconstruction quality.
- Adding perceptual guidance increases computational overhead and training complexity, especially when handling multiple modalities.
- In scenarios with severe occlusion or extremely limited views, the perceptual signals may provide limited guidance, necessitating further integration with multi-view or contextual priors.
Future Work
Future research will explore adaptive multi-modal fusion strategies to dynamically weight perceptual signals based on scene complexity. Extending the framework to handle dynamic scenes and temporal data, as well as improving perception models’ robustness across diverse categories, are promising directions. Additionally, optimizing computational efficiency for real-time applications remains an ongoing challenge.
AI Executive Summary
Single-view 3D reconstruction remains a fundamental challenge in computer vision due to its inherently ill-posed nature. Traditional approaches rely heavily on geometric priors or large-scale generative models, which often struggle to produce semantically consistent and structurally accurate results, especially from limited input data. Inspired by human visual perception, recent advances have begun to incorporate high-level perceptual understanding into the reconstruction process. This paper introduces a novel, perception-guided framework that leverages pretrained models—such as vision-language models (VLMs) and depth estimators—to infuse rich semantic and geometric knowledge into the reconstruction pipeline.
The core idea is to use a generation-perception alignment module (GPAM) that aligns perceptual features with the features within a diffusion-based multi-view image synthesis process. By integrating perceptual signals—semantic descriptions derived from language models and depth features from depth sensing models—the framework guides the generative process towards producing multi-view images that are more semantically and geometrically faithful. This approach is model-agnostic, compatible with various existing reconstruction pipelines like Wonder3D and Era3D.
Experimental results on the Objaverse dataset demonstrate the effectiveness of the method. The combined perception guidance reduced Chamfer distance by up to 32.7%, with improvements across IoU, PSNR, and SSIM metrics. Ablation studies confirmed that multi-modal perception signals are complementary, with perceptual losses like CKA further enhancing performance. The method’s ability to recover fine details and structural accuracy highlights its potential to address longstanding issues in single-view 3D reconstruction.
This work marks a significant step toward integrating perceptual understanding into generative 3D modeling, bridging the gap between high-level perception and low-level geometry. It opens avenues for more robust, semantically consistent, and real-time 3D scene understanding, with broad implications for AR/VR, robotics, and digital content creation. Future directions include adaptive multi-modal fusion, dynamic scene reconstruction, and improving perception model robustness, promising a new era of perception-driven 3D modeling.
Deep Dive
Abstract
The relationship between object perception and reconstruction is well established in human vision, yet remains underexplored in computer vision. In this paper, we demonstrate that learnt object perception can significantly enhance 3D reconstruction. Focusing on the challenging task of single-view 3D object reconstruction, we propose a method that leverages perceptual signals extracted from pretrained perception models capturing semantic and geometric information to drive the reconstruction of an object from its single image. Our approach is model-agnostic and can be integrated into various reconstruction methods in a plug-and-play manner. Experiments with two state-of-the-art single-view 3D reconstruction pipelines in a benchmark dataset show consistent and substantial improvements achieved by our method, validating the effectiveness of incorporating perception into generation. We provide in-depth analysis of various aspects of our method and its application. Our project page is at https://ynhuhuynh.github.io/perception-3d/.