Denoising 3D point cloud strives to remove noise from noisy data. Existing methods address the problem by estimating point-wise displacement from the point feature or by learning the distribution of noise. In this paper, we propose to embed the point cloud through a novel multi-resolution hash encoding, and utilize the embedding to learn an optimum transport plan between noisy and corresponding clean point cloud via transformer encoder and a shared-MLP based decoder. The multi-resolution hash encoding uses hierarchical hash-based representations to efficiently capture geometric details at multiple resolutions. Hence, it enables removal of noise while encoding global-to-local structural details. The transformer encoder further improves the model’s ability to learn long-range dependencies and contextual relationships, facilitating improved denoising performance. The optimum transport plan is devised by simulating a denoising diffusion probabilistic model through Schrödinger bridge problem. The proposed method advances state-of-the-art methods through extensive experiments and offers new insights into the synergy between hash encoding, and transformer architectures in the diffusion framework.

Point cloud completion aims to reconstruct complete 3D shapes from partial observations, often requiring multiple views or complete data for training. In this paper, we propose an attention-driven, self-supervised autoencoder network that completes 3D point clouds from a single partial observation. Multi-head self-attention captures robust contextual relationships, while residual connections in the autoencoder enhance geometric feature learning. In addition to this, we incorporate a contrastive learning-based loss, which encourages the network to better distinguish structural patterns even in highly incomplete observations. Experimental results on benchmark datasets demonstrate that the proposed approach achieves state-of-the-art performance in single-view point cloud completion.

3D point cloud completion problem deals with completing the shape from partial points. The problem finds its application in many vision-related applications. Here, structure plays an important role. Most of the existing approaches either do not consider structural information or consider structure at the decoder only. For maintaining the structure, it is also necessary to maintain the position of the available 3D points. However, most of the approaches lack the aspect of maintaining the available structural position. In this paper, we propose to employ stacked auto-encoder in conjunction a with shared Multi-Layer Perceptron (MLP). MLP converts each 3D point into a feature vector and the stacked auto-encoder helps in maintaining the available structural position of the input points. Further, it explores the redundancy present in the feature vector. It aids to incorporate coarse to fine scale information that further helps in better shape representation. The embedded feature is finally decoded by a structural preserving decoder. Both the encoding and the decoding operations of our method take care of preserving the structure of the available shape information. The experimental results demonstrate the structure preserving capability of our network as compared to the state-of-the-art methods.

We address the task of estimating depth from a single intensity image via a novel convolutional neural network (CNN) encoder-decoder architecture, which learns the depth information using example pairs of color images and their corresponding depth maps. The proposed model integrates residual connections within pooling and up-sampling layers, and hourglass networks which operate on the encoded features, thus processing these at various scales. Furthermore, the model is optimized under the constraints of perceptual as well as the mean squared error loss. The perceptual loss considers the high-level features, thus operating at a different scale of abstraction, which is complementary to the mean squared error loss. The improvements in qualitative and quantitative comparisons with state-of-the-art approaches demonstrate the effectiveness of our approach, even in presence of noise.