The goal of this paper is to encode a 3D scene into an extremely compact representation from$2 D$images and to enable its transmittance, decoding and rendering in real-time across various platforms. Despite the progress in NeRFs and Gaussian Splats, their large model size and specialized renderers make it challenging to distribute free-viewpoint 3D content as easily as images. To address this, we have designed a novel 3D representation that encodes the plenoptic function into sinusoidal function indexed dense volumes. This approach facilitates feature sharing across different locations, improving compactness over traditional spatial voxels. The memory footprint of the dense 3D feature grid can be further reduced using spatial decomposition techniques. This design combines the strengths of spatial hashing functions and voxel decomposition, resulting in a model size as small as 150 KB for each 3D scene. Moreover, PPNG features a lightweight rendering pipeline with only 300 lines of code that decodes its representation into standard GL textures and fragment shaders. This enables realtime rendering using the traditional GL pipeline, ensuring universal compatibility and efficiency across various platforms without additional dependencies. Our results are available at: https://jyl.kr/ppng

The latest regularized Neural Radiance Field (NeRF) approaches produce poor geometry and view extrapolation for large scale sparse view scenes, such as ETH3D. Density-based approaches tend to be under-constrained, while surface-based approaches tend to miss details. In this paper, we take a density-based approach, sampling patches instead of individual rays to better incorporate monocular depth and normal estimates and patch-based photometric consistency constraints between training views and sampled virtual views. Loosely constraining densities based on estimated depth aligned to sparse points further improves geometric accuracy. While maintaining similar view synthesis quality, our approach significantly improves geometric accuracy on the ETH3D benchmark, e.g. increasing the F1@2cm score by 4x-8x compared to other regularized density-based approaches, with much lower training and inference time than other approaches.

We investigate whether region-based representations are effective for recognition. Regions were once a mainstay in recognition approaches, but pixel and patch-based features are now used almost exclusively. We show that recent class-agnostic segmenters like SAM can be effectively combined with strong self-supervised representations, like those from DINOv2, and used for a wide variety of tasks, including semantic segmentation, object-based image re-trieval, and multi-image analysis. Once the masks and features are extracted, these representations, even with linear decoders, enable competitive performance, making them well suited to applications that require custom queries. The representations' compactness also makes them well-suited to video analysis and other problems requiring inference across many images.

DIVeR builds on the key ideas of NeRF and its variants-density models and volume rendering – to learn 3D object models that can be rendered realistically from small numbers of images. In contrast to all previous NeRF methods, DIVeR uses deterministic rather than stochastic estimates of the volume rendering integral. DIVeR's representation is a voxel based field of features. To compute the volume rendering integral, a ray is broken into intervals, one per voxel; components of the volume rendering integral are estimated from the features for each interval using an MLP, and the components are aggregated. As a result, DIVeR can render thin translucent structures that are missed by other integrators. Furthermore, DIVeR's representation has semantics that is relatively exposed compared to other such methods – moving feature vectors around in the voxel space results in natural edits. Extensive qualitative and quantitative comparisons to current state-of-the-art methods show that DIVeR produces models that (1) render at or above state-of-the-art quality, (2) are very small without being baked, (3) render very fast without being baked, and (4) can be edited in natural ways. Our real-time code is available at: https://github.com/lwwu2/diver-rt

We address estimating dense correspondences between two images depicting different but semantically related scenes. End-to-end trainable deep neural networks incorporating neighborhood consensus cues are currently the best methods for this task. However, these architectures require exhaustive matching and 4D convolutions over matching costs for all pairs of feature map pixels. This makes them computationally expensive. We present a more efficient neighborhood consensus approach based on PatchMatch. For higher accuracy, we propose to use a learned local 4D scoring function for evaluating candidates during the PatchMatch iterations. We have devised an approach to jointly train the scoring function and the feature extraction modules by embedding them into a proxy model which is end-to-end differentiable. The modules are trained in a supervised setting using a cross-entropy loss to directly incorporate sparse keypoint supervision. Our evaluation on PF-Pascal and SPair-71K shows that our method significantly outperforms the state-of-the-art on both datasets while also being faster and using less memory.

Recent learning-based multi-view stereo (MVS) methods show excellent performance with dense cameras and small depth ranges. However, non-learning based approaches still outperform for scenes with large depth ranges and sparser wide-baseline views, in part due to their PatchMatch optimization over pixelwise estimates of depth, normals, and visibility. In this paper, we propose an end-to-end trainable PatchMatch-based MVS approach that combines advantages of trainable costs and regularizations with pixelwise estimates. To overcome the challenge of the non-differentiable PatchMatch optimization that involves iterative sampling and hard decisions, we use reinforcement learning to minimize expected photometric cost and maximize likelihood of ground truth depth and normals. We incorporate normal estimation by using dilated patch kernels and propose a recurrent cost regularization that applies beyond frontal plane-sweep algorithms to our pixelwise depth/normal estimates. We evaluate our method on widely used MVS benchmarks, ETH3D and Tanks and Temples (TnT). On ETH3D, our method outperforms other recent learning-based approaches and performs comparably on advanced TnT.

We present FEATS (Feature Extraction and Tracking Simulator), that synthesizes feature tracks using a camera trajectory and scene geometry (e.g. CAD, multi-view stereo). We introduce 2D feature and matching noise models that can be controlled using a few parameters. We also provide a new dataset of images and ground truth camera pose. We process this data (and a synthetic version) with several current SfM algorithms and show that the synthetic tracks are representative of the real tracks. We then show two practical uses of FEATS: (1) we generate hundreds of trajectories with varying noise and show that COLMAP is more robust to noise than OpenSfM and VisualSfM; and (2) we calculate 3D point error and show that accurate camera pose estimates do not guarantee accurate 3D maps.