Dense RepPoints: Representing Visual Objects with Dense Point Sets (1912.11473v3)

Abstract: We present a new object representation, called Dense RepPoints, that utilizes a large set of points to describe an object at multiple levels, including both box level and pixel level. Techniques are proposed to efficiently process these dense points, maintaining near-constant complexity with increasing point numbers. Dense RepPoints is shown to represent and learn object segments well, with the use of a novel distance transform sampling method combined with set-to-set supervision. The distance transform sampling combines the strengths of contour and grid representations, leading to performance that surpasses counterparts based on contours or grids. Code is available at \url{https://github.com/justimyhxu/Dense-RepPoints}.

• The paper presents Dense RepPoints, a novel approach that uses dense point sets to capture fine-grained geometric and semantic details for enhanced object segmentation.
• The method integrates Distance Transform Sampling and set-to-set supervision to efficiently manage complex instance segmentation tasks while controlling computational costs.
• Empirical evaluations demonstrate superior performance on the COCO dataset with a ResNet-101 backbone, achieving 39.1 mask mAP and 45.6 box mAP.

Dense RepPoints: Enhancing Object Representation with Dense Point Sets

The paper "Dense RepPoints: Representing Visual Objects with Dense Point Sets" introduces a novel object representation paradigm known as Dense RepPoints. This framework, developed by Yang et al., presents an innovative approach to improving object segmentation by utilizing dense point sets for comprehensive geometric and semantic descriptions across multiple levels of granularity, ranging from box level to pixel level. The methodology leverages efficient processing techniques to handle large numbers of points while managing computational complexity effectively.

Dense RepPoints builds upon the conceptual foundation of the RepPoints framework, which employs a smaller set of adaptive points for object representation purposes. While RepPoints have demonstrated efficacy in tasks such as object detection, the limited number of points in the original model has restricted its capacity to capture fine-grained geometric structures needed for complex tasks like instance segmentation.

The methodology proposed in this paper involves several key innovations:

1. Distance Transform Sampling (DTS): The authors propose a novel sampling strategy that combines strengths from traditional contour and grid representations. DTS generates point samples based on the distance from the object boundary, leading to an efficient yet detailed segmentation model.
2. Set-to-Set Supervision: Unlike the conventional point-to-point supervision, set-to-set supervision evaluates the Chamfer distance across point sets, allowing for a more flexible geometric description that is particularly beneficial for tasks such as instance segmentation where exact point correspondences are challenging to establish.
3. Efficient Processing Techniques: The paper introduces group pooling and shared attribute fields which help in maintaining near-constant computational complexity irrespective of the number of dense points. This ensures scalability and efficiency of the Dense RepPoints framework even as the number of points increases significantly.

The empirical evaluation conducted by the authors demonstrates the superior performance of Dense RepPoints over existing methods in the COCO dataset, achieving an impressive 39.1 mask mAP and 45.6 box mAP using a ResNet-101 backbone. Such results underscore the efficacy of the proposed dense representation strategy when integrated with high-performing backbone architectures.

Practical implications of Dense RepPoints are profound, making it a versatile candidate for various computer vision tasks that require high precision and adaptability in object representation. The ability to model different segment descriptors effectively, such as binary boundary masks, is likely to inspire further research into hybrid model architectures that leverage various representation forms within a unified framework.

From a theoretical standpoint, the development of Dense RepPoints emphasizes the growing need for efficient object representations that can handle intricate segmentation tasks without incurring prohibitive computational costs. Future research could explore integrations with advanced backbone models and novel post-processing techniques to enhance accuracy further. Additionally, extending this approach to three-dimensional object representations may open new avenues for exploration in fields such as autonomous driving, augmented reality, and complex scene understanding.

In summary, Dense RepPoints marks a significant advancement in visual object representation by striking a balance between representation richness and computational feasibility, paving the way for more intricate and efficient computer vision models.