NeuralDiff: Segmenting 3D objects that move in egocentric videos (2110.09936v1)

Abstract: Given a raw video sequence taken from a freely-moving camera, we study the problem of decomposing the observed 3D scene into a static background and a dynamic foreground containing the objects that move in the video sequence. This task is reminiscent of the classic background subtraction problem, but is significantly harder because all parts of the scene, static and dynamic, generate a large apparent motion due to the camera large viewpoint change. In particular, we consider egocentric videos and further separate the dynamic component into objects and the actor that observes and moves them. We achieve this factorization by reconstructing the video via a triple-stream neural rendering network that explains the different motions based on corresponding inductive biases. We demonstrate that our method can successfully separate the different types of motion, outperforming recent neural rendering baselines at this task, and can accurately segment moving objects. We do so by assessing the method empirically on challenging videos from the EPIC-KITCHENS dataset which we augment with appropriate annotations to create a new benchmark for the task of dynamic object segmentation on unconstrained video sequences, for complex 3D environments.

• The paper introduces a triple-stream neural rendering network that segments static backgrounds, dynamic objects, and the actor in egocentric video sequences.
• It demonstrates superior segmentation performance with improved mAP and PSNR metrics on the EPIC-KITCHENS dataset.
• The approach paves the way for advanced AR and robotics applications by modeling complex, dynamic 3D scenes from moving camera perspectives.

Analysis of NeuralDiff: Segmenting 3D Objects in Egocentric Videos

In this paper, the authors present NeuralDiff, a novel neural architecture for segmenting dynamic 3D objects in egocentric video sequences. The primary challenge tackled is decomposing a video into static backgrounds and dynamic foregrounds, particularly when both components exhibit significant apparent motion due to camera movement. This segmentation task is fundamentally more complex than traditional background subtraction, given the egocentric nature of the videos, which involve substantial viewpoint changes and parallax effects.

Methodology

The authors introduce a triple-stream neural rendering network to address this segmentation challenge, where each stream is designed to model a specific component of the scene: the static background, dynamic objects, and the actor. These streams leverage different inductive biases to effectively separate and reconstruct each component in 3D.

1. Background Stream: This is tasked with reconstructing the static components of the scene, which serve as the baseline against which dynamic objects are discerned.
2. Foreground Stream: This stream captures the dynamic objects, those manipulated during the interaction. A notable aspect of this stream is its use of temporal encoding to handle the infrequent motion of objects, avoiding the assumption of constant movement.
3. Actor Stream: Unique to the egocentric video context, this stream models the observing actor's body, which is continuously moving. By expressing the actor's body's dynamics in the camera's reference frame, the model appropriately captures the occlusion effects pertinent to egocentric viewpoints.

The architecture facilitates the synthesis and segmentation of video frames from unobserved viewpoints, challenging conventional methods that typically rely on single-view correspondences or static assumptions. This is achieved through a volumetric sampling process enhanced with a sophisticated probabilistic color mixing model and uncertainty modeling, which is carefully integrated into the rendering framework.

Results

The paper reports empirical results on the EPIC-KITCHENS dataset, augmented with annotations to create the EPIC-Diff benchmark, specifically targeting the segmentation of dynamic objects in complex 3D environments. NeuralDiff demonstrates superior performance in terms of segmentation mean Average Precision (mAP) and Peak Signal-to-Noise Ratio (PSNR) for rendering quality, establishing advancements over NeRF and its variant NeRF-W.

The approach excels in segmenting dynamic objects and actors even in prolonged video sequences, addressing scenarios where traditional methods fall short due to considerable camera motion and sporadic object movement. The improvements from integrating an actor model and refined color mixing mechanisms not only enhance segmentation accuracy but also the perceptual quality of synthesized frames.

Implications and Future Directions

Practically, NeuralDiff's ability to autonomously segment and understand dynamic components in egocentric video extends possibilities in augmented reality (AR), human-computer interaction, and video-based robotics applications. The capacity to interpret and synthesize complex scenes from moving observer perspectives positions this work as a stepping stone toward richer, unsupervised understanding of interaction-heavy environments.

Theoretically, this work underscores the potential of neural rendering techniques as powerful tools beyond photorealistic synthesis, expanding their scope to critical analysis tasks in computer vision. Future research might explore the integration of more sophisticated temporal dynamics and broader contextual learning to further enhance the semantic understanding in neural rendering frameworks. Additionally, exploring multi-view training scenarios could unlock more generalizable models, reducing the dependency on egocentric constraints for broader scene understanding applications.

In conclusion, NeuralDiff presents a robust methodology for addressing the inherent complexities of segmenting dynamic objects in egocentric videos. The research contributes both innovative technical solutions and new benchmarks for advancing the field, marking a significant step in leveraging neural rendering for 3D scene understanding.