NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection (2307.14620v1)

Abstract: We present NeRF-Det, a novel method for indoor 3D detection with posed RGB images as input. Unlike existing indoor 3D detection methods that struggle to model scene geometry, our method makes novel use of NeRF in an end-to-end manner to explicitly estimate 3D geometry, thereby improving 3D detection performance. Specifically, to avoid the significant extra latency associated with per-scene optimization of NeRF, we introduce sufficient geometry priors to enhance the generalizability of NeRF-MLP. Furthermore, we subtly connect the detection and NeRF branches through a shared MLP, enabling an efficient adaptation of NeRF to detection and yielding geometry-aware volumetric representations for 3D detection. Our method outperforms state-of-the-arts by 3.9 mAP and 3.1 mAP on the ScanNet and ARKITScenes benchmarks, respectively. We provide extensive analysis to shed light on how NeRF-Det works. As a result of our joint-training design, NeRF-Det is able to generalize well to unseen scenes for object detection, view synthesis, and depth estimation tasks without requiring per-scene optimization. Code is available at \url{https://github.com/facebookresearch/NeRF-Det}.

• The paper introduces an integrated NeRF branch with 3D detection that jointly trains geometry-aware representations, eliminating the need for depth sensors.
• It leverages an end-to-end shared MLP and augmented priors to bypass per-scene optimization while improving indoor object detection performance.
• Evaluated on ScanNet and ARKITScenes, NeRF-Det achieves gains of up to 3.9 mAP and 3.1 mAP over state-of-the-art methods.

Summary of "NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection"

The paper "NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection" introduces a novel approach aimed at enhancing indoor 3D object detection processes through the integration of Neural Radiance Fields (NeRF). This work mainly focuses on tackling the challenges inherent in RGB-only 3D detection tasks by leveraging NeRF to improve the modeling of scene geometry.

Key Contributions

The authors propose a technique that combines 3D object detection with NeRF, an approach previously restricted by its high latency in per-scene optimization. The authors innovate by embedding a NeRF branch, which is jointly trained with the detection branch, utilizing shared geometry-aware volumetric representations to eliminate the requirement for additional depth sensors. Specifically, the paper emphasizes several key strategies:

• End-to-End Training: Implementing a shared Multi-Layer Perceptron (MLP) allows both the detection and NeRF branches to benefit from an end-to-end training process. By connecting these branches, gradients are effectively back-propagated to improve detection performance.
• Generalizable NeRF Modeling: To circumvent the computational cost typically associated with NeRF’s per-scene optimization, this approach leverages augmented priors for the NeRF MLP, making it applicable across varying scenes.
• Explicit Scene Geometry Modelling: A novel transformation of ray-based density measurements into an opacity field serves to embed geometric cues directly within the volumetric representation utilized by the detection pipeline.

Performance and Evaluation

Utilizing the ScanNet and ARKITScenes datasets as benchmarks, the authors report a performance enhancement of 3.9 mAP and 3.1 mAP, respectively, when compared to state-of-the-art baselines. This substantial improvement underscores the efficacy of geometry-aware volumetric representations for RGB-based 3D object detection. The paper demonstrates that NeRF-Det can effectively perform novel view synthesis and depth estimation, achieving robust generalizability across previously unseen scenes. These findings highlight NeRF-Det’s ability to offer latency-efficient performance improvements without requiring per-scene optimization.

Implications and Future Directions

The implications of NeRF-Det are multifaceted, primarily impacting applications where RGB-only input is predominant due to constraints like cost and form factor, such as in AR/VR systems and mobile devices. By bridging the gap between sophisticated scene geometry estimation and real-time 3D detection, this work paves the way for advanced interactive applications in indoor settings.

From a theoretical standpoint, NeRF-Det emphasizes the potential of a synergistic NeRF framework to be utilized within larger, more complex real-time systems. This marks progress towards achieving more generalized 3D understanding from 2D representations without the direct reliance on depth data.

Future research endeavors could explore extensions of this technique to outdoor environments and dynamic scenes, where the constraints and challenges vary significantly. Additionally, efforts may be directed towards further reducing computational demands or exploring additional use cases, such as real-time robotics and autonomous navigation systems, which would benefit substantially from such refined 3D perception capabilities.

Thus, NeRF-Det represents a notable advancement in the field of computer vision, highlighting the versatility and untapped potential of NeRF within practical 3D detection tasks. The shared learnings and insights from this paper have the potential to instigate further research into the integration of real-time, photo-realistic 3D representations from multi-view inputs.