RGBD Video Segmentation via Two-Branch Temporal Geometric Consistency
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Solution Overview
Problem
Current RGBD semantic segmentation methods fail to maintain consistency across video frames, lacking temporal and geometric coherence, which is crucial for applications like autonomous driving that require situational awareness and scene understanding.
Innovation Solution
A two-branch network architecture is introduced, with a fast branch for quick updates in RGB and depth channels across video frames and a slow branch for detailed segmentation on key frames, incorporating alternating modality processing and warping models to ensure temporal and geometric consistency, using ego-motion flow and projected scene flow to align frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image-based RGBD semantic segmentation methods are used, then processing speed is improved, but temporal and geometric consistency across video frames deteriorates
Solution Approach 1:
The video processing is segmented into two distinct branches: a fast branch that processes individual frames independently for speed, and a slow branch that processes keyframes with temporal alignment for consistency. This segmentation allows each branch to optimize for its specific function while working together to achieve both speed and consistency goals.
Solution Approach 2:
The system performs preliminary temporal and geometric alignment on keyframes before detailed semantic segmentation. By pre-aligning the keyframes using ego-motion flow and projected scene flow, the system establishes a consistent temporal and geometric framework that guides subsequent frame processing, ensuring consistency without reprocessing every frame in detail.
2Measurement precision
If detailed semantic segmentation is performed on every frame, then segmentation accuracy is improved, but processing time increases
Solution Approach 1:
The system applies detailed semantic segmentation only to keyframes rather than every frame. By performing comprehensive processing on a subset of frames (keyframes) and using temporal propagation for intermediate frames, the system achieves sufficient accuracy without the time cost of processing every frame at full detail.
Solution Approach 2:
The system creates temporal copies of segmentation results from keyframes and propagates them to intermediate frames. By copying the detailed segmentation information from aligned keyframes and adapting it to neighboring frames through temporal propagation, the system maintains accuracy while significantly reducing processing time.
3Reliability
If temporal and geometric alignment is performed, then consistency across frames is improved, but computational complexity increases
Solution Approach 1:
The alignment computation is segmented to be performed only on keyframes rather than all frames. By dividing the video sequence into keyframes and intermediate frames, the system performs the computationally intensive alignment operations only where necessary, reducing overall complexity while maintaining consistency through temporal propagation.
Solution Approach 2:
The system performs temporal and geometric alignment periodically at keyframes rather than continuously at every frame. This periodic alignment approach, combined with temporal propagation for intermediate frames, maintains consistency across the video sequence while significantly reducing the frequency and total computational burden of alignment operations.
Data Source
AI summary
A method, machine readable medium and system for RGBD semantic segmentation of video data includes determining semantic segmentation data and depth segmentation data for less than all classes for images of each frame of a first video, determining semantic segmentation data and depth segmentation data for images of each key frame of a second video including a synchronous combination of respective frames of the RGB video and the depth-aware video in parallel to the determination of the semantic segmentation data and the depth segmentation data for each frame of the first video, temporally and geometrically aligning respective frames of the first video and the second video, and predicting semantic segmentation data and depth segmentation data for images of a subsequent frame of the first video based on the determination of the semantic segmentation data and depth segmentation data for images of a key frame of the second video.